2710 lines
195 KiB
Plaintext
2710 lines
195 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/javascript": [
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"\n",
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" setTimeout(function() {\n",
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" var nbb_cell_id = 1;\n",
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" var nbb_unformatted_code = \"%load_ext nb_black\";\n",
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" var nbb_formatted_code = \"%load_ext nb_black\";\n",
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" var nbb_cells = Jupyter.notebook.get_cells();\n",
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" for (var i = 0; i < nbb_cells.length; ++i) {\n",
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" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
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" nbb_cells[i].set_text(nbb_formatted_code);\n",
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" }\n",
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" }\n",
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" }, 500);\n",
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"<IPython.core.display.Javascript object>"
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"%load_ext nb_black"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"code_folding": []
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},
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"outputs": [
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{
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"data": {
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"application/javascript": [
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"\n",
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" setTimeout(function() {\n",
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" var nbb_cell_id = 2;\n",
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" var nbb_unformatted_code = \"# Import libraries\\nfrom __future__ import division\\n\\nimport pandas as pd\\nimport matplotlib.pyplot as plt # plotting\\nimport numpy as np # linear algebra\\nimport math\\n\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.svm import SVC\\n\\nfrom sklearn.pipeline import make_pipeline\\n\\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\\n\\nfrom sklearn.model_selection import (\\n train_test_split,\\n GridSearchCV,\\n cross_val_score,\\n RepeatedStratifiedKFold,\\n StratifiedKFold,\\n)\\n\\nfrom sklearn.metrics import (\\n accuracy_score,\\n confusion_matrix,\\n roc_curve,\\n roc_auc_score,\\n auc,\\n precision_score,\\n recall_score,\\n precision_recall_curve,\\n plot_confusion_matrix,\\n f1_score,\\n)\\n\\n\\nfrom collections import Counter\\n\\nfrom sklearn.datasets import make_classification\\n\\nimport copy\\n\\nimport warnings\";\n",
|
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" var nbb_formatted_code = \"# Import libraries\\nfrom __future__ import division\\n\\nimport pandas as pd\\nimport matplotlib.pyplot as plt # plotting\\nimport numpy as np # linear algebra\\nimport math\\n\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.svm import SVC\\n\\nfrom sklearn.pipeline import make_pipeline\\n\\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\\n\\nfrom sklearn.model_selection import (\\n train_test_split,\\n GridSearchCV,\\n cross_val_score,\\n RepeatedStratifiedKFold,\\n StratifiedKFold,\\n)\\n\\nfrom sklearn.metrics import (\\n accuracy_score,\\n confusion_matrix,\\n roc_curve,\\n roc_auc_score,\\n auc,\\n precision_score,\\n recall_score,\\n precision_recall_curve,\\n plot_confusion_matrix,\\n f1_score,\\n)\\n\\n\\nfrom collections import Counter\\n\\nfrom sklearn.datasets import make_classification\\n\\nimport copy\\n\\nimport warnings\";\n",
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" var nbb_cells = Jupyter.notebook.get_cells();\n",
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" for (var i = 0; i < nbb_cells.length; ++i) {\n",
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" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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" }\n",
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" break;\n",
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" }\n",
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" }\n",
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" }, 500);\n",
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" "
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],
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"text/plain": [
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"<IPython.core.display.Javascript object>"
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
||
"# Import libraries\n",
|
||
"from __future__ import division\n",
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"\n",
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||
"import pandas as pd\n",
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||
"import matplotlib.pyplot as plt # plotting\n",
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"import numpy as np # linear algebra\n",
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"import math\n",
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"\n",
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"from sklearn.linear_model import LogisticRegression\n",
|
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"from sklearn.svm import SVC\n",
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"\n",
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"from sklearn.pipeline import make_pipeline\n",
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"\n",
|
||
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
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"\n",
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||
"from sklearn.model_selection import (\n",
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" train_test_split,\n",
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" GridSearchCV,\n",
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||
" cross_val_score,\n",
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" RepeatedStratifiedKFold,\n",
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" StratifiedKFold,\n",
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")\n",
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||
"\n",
|
||
"from sklearn.metrics import (\n",
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||
" accuracy_score,\n",
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||
" confusion_matrix,\n",
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||
" roc_curve,\n",
|
||
" roc_auc_score,\n",
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||
" auc,\n",
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||
" precision_score,\n",
|
||
" recall_score,\n",
|
||
" precision_recall_curve,\n",
|
||
" plot_confusion_matrix,\n",
|
||
" f1_score,\n",
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||
")\n",
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"\n",
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"\n",
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||
"from collections import Counter\n",
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"\n",
|
||
"from sklearn.datasets import make_classification\n",
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||
"\n",
|
||
"import copy\n",
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||
"\n",
|
||
"import warnings"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": []
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
|
||
"# Data"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
|
||
"### File path"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 3,
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||
"metadata": {},
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"outputs": [
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{
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"data": {
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"application/javascript": [
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"\n",
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" setTimeout(function() {\n",
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||
" var nbb_cell_id = 3;\n",
|
||
" var nbb_unformatted_code = \"folder = \\\"../pkg/flowOutput/\\\"\\nfname_benign = \\\"2017-05-02_kali-normal22_flow_stats.csv\\\"\\nfname_malicious = \\\"webgoat_flow_stats.csv\\\"\";\n",
|
||
" var nbb_formatted_code = \"folder = \\\"../pkg/flowOutput/\\\"\\nfname_benign = \\\"2017-05-02_kali-normal22_flow_stats.csv\\\"\\nfname_malicious = \\\"webgoat_flow_stats.csv\\\"\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
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" for (var i = 0; i < nbb_cells.length; ++i) {\n",
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" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
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" nbb_cells[i].set_text(nbb_formatted_code);\n",
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" }\n",
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" break;\n",
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" }\n",
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" }\n",
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" }, 500);\n",
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" "
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],
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"text/plain": [
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"<IPython.core.display.Javascript object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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||
}
|
||
],
|
||
"source": [
|
||
"folder = \"../pkg/flowOutput/\"\n",
|
||
"fname_benign = \"2017-05-02_kali-normal22_flow_stats.csv\"\n",
|
||
"fname_malicious = \"webgoat_flow_stats.csv\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Malicious: Webgoat"
|
||
]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
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||
"metadata": {
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||
"code_folding": []
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||
},
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||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(248, 40)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 4;\n",
|
||
" var nbb_unformatted_code = \"# Malicious flows\\npd_malicious = pd.read_csv(folder + fname_malicious)\\npd_malicious.drop(pd_malicious.tail(1).index, inplace=True)\\npd_malicious[\\\"Type\\\"] = \\\"Malicious\\\"\\n\\nprint(pd_malicious.shape)\";\n",
|
||
" var nbb_formatted_code = \"# Malicious flows\\npd_malicious = pd.read_csv(folder + fname_malicious)\\npd_malicious.drop(pd_malicious.tail(1).index, inplace=True)\\npd_malicious[\\\"Type\\\"] = \\\"Malicious\\\"\\n\\nprint(pd_malicious.shape)\";\n",
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" var nbb_cells = Jupyter.notebook.get_cells();\n",
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" for (var i = 0; i < nbb_cells.length; ++i) {\n",
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" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
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" nbb_cells[i].set_text(nbb_formatted_code);\n",
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" }\n",
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" }\n",
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" }\n",
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" }, 500);\n",
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" "
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],
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"text/plain": [
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"<IPython.core.display.Javascript object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Malicious flows\n",
|
||
"pd_malicious = pd.read_csv(folder + fname_malicious)\n",
|
||
"pd_malicious.drop(pd_malicious.tail(1).index, inplace=True)\n",
|
||
"pd_malicious[\"Type\"] = \"Malicious\"\n",
|
||
"\n",
|
||
"print(pd_malicious.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Benign"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"code_folding": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(28714, 40)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 5;\n",
|
||
" var nbb_unformatted_code = \"# Benign flows\\npd_benign = pd.read_csv(folder + fname_benign)\\n# pd_benign.drop(pd_webgoat.tail(1).index, inplace=True)\\npd_benign[\\\"Type\\\"] = \\\"Benign\\\"\\n\\nprint(pd_benign.shape)\";\n",
|
||
" var nbb_formatted_code = \"# Benign flows\\npd_benign = pd.read_csv(folder + fname_benign)\\n# pd_benign.drop(pd_webgoat.tail(1).index, inplace=True)\\npd_benign[\\\"Type\\\"] = \\\"Benign\\\"\\n\\nprint(pd_benign.shape)\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
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" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Benign flows\n",
|
||
"pd_benign = pd.read_csv(folder + fname_benign)\n",
|
||
"# pd_benign.drop(pd_webgoat.tail(1).index, inplace=True)\n",
|
||
"pd_benign[\"Type\"] = \"Benign\"\n",
|
||
"\n",
|
||
"print(pd_benign.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Combined dataframe - Benign + malicious"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"code_folding": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(28962, 40)"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 6;\n",
|
||
" var nbb_unformatted_code = \"## Combine malicous and benign dataframes.\\npd_comb = pd.concat([pd_malicious, pd_benign])\\n\\n## Random shuffle of rows\\npd_comb = pd_comb.sample(frac=1)\\n\\npd_comb.shape\";\n",
|
||
" var nbb_formatted_code = \"## Combine malicous and benign dataframes.\\npd_comb = pd.concat([pd_malicious, pd_benign])\\n\\n## Random shuffle of rows\\npd_comb = pd_comb.sample(frac=1)\\n\\npd_comb.shape\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"## Combine malicous and benign dataframes.\n",
|
||
"pd_comb = pd.concat([pd_malicious, pd_benign])\n",
|
||
"\n",
|
||
"## Random shuffle of rows\n",
|
||
"pd_comb = pd_comb.sample(frac=1)\n",
|
||
"\n",
|
||
"pd_comb.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>fiveTuple</th>\n",
|
||
" <th>srcIP</th>\n",
|
||
" <th>dstIP</th>\n",
|
||
" <th>protocol</th>\n",
|
||
" <th>srcPort</th>\n",
|
||
" <th>dstPort</th>\n",
|
||
" <th>flowDuration</th>\n",
|
||
" <th>flowLength</th>\n",
|
||
" <th>fwdFlowLength</th>\n",
|
||
" <th>bwdFlowLength</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>bwdIATTotal</th>\n",
|
||
" <th>fwdIATMean</th>\n",
|
||
" <th>bwdIATMean</th>\n",
|
||
" <th>fwdIATStd</th>\n",
|
||
" <th>bwdIATStd</th>\n",
|
||
" <th>fwdIATMin</th>\n",
|
||
" <th>bwdIATMin</th>\n",
|
||
" <th>fwdIATMax</th>\n",
|
||
" <th>bwdIATMax</th>\n",
|
||
" <th>Type</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>22705</th>\n",
|
||
" <td>216.137.61.254--192.168.1.191--TCP--443(https)...</td>\n",
|
||
" <td>192.168.1.191</td>\n",
|
||
" <td>216.137.61.254</td>\n",
|
||
" <td>TCP</td>\n",
|
||
" <td>39338</td>\n",
|
||
" <td>443(https)</td>\n",
|
||
" <td>5.189759e+09</td>\n",
|
||
" <td>15.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>15.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>5.189759e+09</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>3.706970e+08</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.209897e+09</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>9735.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>4.741535e+09</td>\n",
|
||
" <td>Benign</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24032</th>\n",
|
||
" <td>192.168.1.191--216.137.61.62--TCP--52706--80(h...</td>\n",
|
||
" <td>216.137.61.62</td>\n",
|
||
" <td>192.168.1.191</td>\n",
|
||
" <td>TCP</td>\n",
|
||
" <td>80(http)</td>\n",
|
||
" <td>52706</td>\n",
|
||
" <td>1.095918e+11</td>\n",
|
||
" <td>23.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>23.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1.095918e+11</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>4.981446e+09</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>4.866784e+09</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>6304.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.138703e+10</td>\n",
|
||
" <td>Benign</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4936</th>\n",
|
||
" <td>173.241.240.220--192.168.1.191--TCP--80(http)-...</td>\n",
|
||
" <td>192.168.1.191</td>\n",
|
||
" <td>173.241.240.220</td>\n",
|
||
" <td>TCP</td>\n",
|
||
" <td>47724</td>\n",
|
||
" <td>80(http)</td>\n",
|
||
" <td>1.667897e+09</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1.667897e+09</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>5.212100e+07</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>2.502940e+08</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>5642.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>1.441531e+09</td>\n",
|
||
" <td>Benign</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>3 rows × 40 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" fiveTuple srcIP \\\n",
|
||
"22705 216.137.61.254--192.168.1.191--TCP--443(https)... 192.168.1.191 \n",
|
||
"24032 192.168.1.191--216.137.61.62--TCP--52706--80(h... 216.137.61.62 \n",
|
||
"4936 173.241.240.220--192.168.1.191--TCP--80(http)-... 192.168.1.191 \n",
|
||
"\n",
|
||
" dstIP protocol srcPort dstPort flowDuration \\\n",
|
||
"22705 216.137.61.254 TCP 39338 443(https) 5.189759e+09 \n",
|
||
"24032 192.168.1.191 TCP 80(http) 52706 1.095918e+11 \n",
|
||
"4936 173.241.240.220 TCP 47724 80(http) 1.667897e+09 \n",
|
||
"\n",
|
||
" flowLength fwdFlowLength bwdFlowLength ... bwdIATTotal \\\n",
|
||
"22705 15.0 0.0 15.0 ... 5.189759e+09 \n",
|
||
"24032 23.0 0.0 23.0 ... 1.095918e+11 \n",
|
||
"4936 33.0 0.0 33.0 ... 1.667897e+09 \n",
|
||
"\n",
|
||
" fwdIATMean bwdIATMean fwdIATStd bwdIATStd fwdIATMin \\\n",
|
||
"22705 0.0 3.706970e+08 0.0 1.209897e+09 0.0 \n",
|
||
"24032 0.0 4.981446e+09 0.0 4.866784e+09 0.0 \n",
|
||
"4936 0.0 5.212100e+07 0.0 2.502940e+08 0.0 \n",
|
||
"\n",
|
||
" bwdIATMin fwdIATMax bwdIATMax Type \n",
|
||
"22705 9735.0 0.0 4.741535e+09 Benign \n",
|
||
"24032 6304.0 0.0 1.138703e+10 Benign \n",
|
||
"4936 5642.0 0.0 1.441531e+09 Benign \n",
|
||
"\n",
|
||
"[3 rows x 40 columns]"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 7;\n",
|
||
" var nbb_unformatted_code = \"pd_comb.head(3)\";\n",
|
||
" var nbb_formatted_code = \"pd_comb.head(3)\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd_comb.head(3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Added throughput columns."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"flowLengthPerTime\n",
|
||
"fwdFlowLengthPerTime\n",
|
||
"bwdFlowLengthPerTime\n",
|
||
"packetSizeTotalPerTime\n",
|
||
"fwdPacketSizeTotalPerTime\n",
|
||
"bwdPacketSizeTotalPerTime\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 8;\n",
|
||
" var nbb_unformatted_code = \"## Add throughput columns.\\ncolsPerTime = [\\n \\\"flowLength\\\",\\n \\\"fwdFlowLength\\\",\\n \\\"bwdFlowLength\\\",\\n \\\"packetSizeTotal\\\",\\n \\\"fwdPacketSizeTotal\\\",\\n \\\"bwdPacketSizeTotal\\\",\\n]\\n\\nfor feature in colsPerTime:\\n pd_comb[feature + \\\"PerTime\\\"] = pd_comb[feature] / pd_comb[\\\"flowDuration\\\"]\\n print(feature + \\\"PerTime\\\")\";\n",
|
||
" var nbb_formatted_code = \"## Add throughput columns.\\ncolsPerTime = [\\n \\\"flowLength\\\",\\n \\\"fwdFlowLength\\\",\\n \\\"bwdFlowLength\\\",\\n \\\"packetSizeTotal\\\",\\n \\\"fwdPacketSizeTotal\\\",\\n \\\"bwdPacketSizeTotal\\\",\\n]\\n\\nfor feature in colsPerTime:\\n pd_comb[feature + \\\"PerTime\\\"] = pd_comb[feature] / pd_comb[\\\"flowDuration\\\"]\\n print(feature + \\\"PerTime\\\")\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"## Add throughput columns.\n",
|
||
"colsPerTime = [\n",
|
||
" \"flowLength\",\n",
|
||
" \"fwdFlowLength\",\n",
|
||
" \"bwdFlowLength\",\n",
|
||
" \"packetSizeTotal\",\n",
|
||
" \"fwdPacketSizeTotal\",\n",
|
||
" \"bwdPacketSizeTotal\",\n",
|
||
"]\n",
|
||
"\n",
|
||
"for feature in colsPerTime:\n",
|
||
" pd_comb[feature + \"PerTime\"] = pd_comb[feature] / pd_comb[\"flowDuration\"]\n",
|
||
" print(feature + \"PerTime\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Features"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 9;\n",
|
||
" var nbb_unformatted_code = \"# Feature columns.\\nfeature_cols = [\\n \\\"flowDuration\\\",\\n \\\"flowLength\\\",\\n \\\"fwdFlowLength\\\",\\n \\\"bwdFlowLength\\\",\\n \\\"packetSizeTotal\\\",\\n \\\"packetSizeMean\\\",\\n \\\"packetSizeStd\\\",\\n \\\"packetSizeMin\\\",\\n \\\"packetSizeMax\\\",\\n \\\"fwdPacketSizeTotal\\\",\\n \\\"bwdPacketSizeTotal\\\",\\n \\\"fwdPacketSizeMean\\\",\\n \\\"bwdPacketSizeMean\\\",\\n \\\"fwdPacketSizeStd\\\",\\n \\\"bwdPacketSizeStd\\\",\\n \\\"fwdPacketSizeMin\\\",\\n \\\"bwdPacketSizeMin\\\",\\n \\\"fwdPacketSizeMax\\\",\\n \\\"bwdPacketSizeMax\\\",\\n \\\"IATMean\\\",\\n \\\"IATStd\\\",\\n \\\"IATMin\\\",\\n \\\"IATMax\\\",\\n \\\"fwdIATTotal\\\",\\n \\\"bwdIATTotal\\\",\\n \\\"fwdIATMean\\\",\\n \\\"bwdIATMean\\\",\\n \\\"fwdIATStd\\\",\\n \\\"bwdIATStd\\\",\\n \\\"fwdIATMin\\\",\\n \\\"bwdIATMin\\\",\\n \\\"fwdIATMax\\\",\\n \\\"bwdIATMax\\\",\\n \\\"flowLengthPerTime\\\",\\n \\\"fwdFlowLengthPerTime\\\",\\n \\\"bwdFlowLengthPerTime\\\",\\n \\\"packetSizeTotalPerTime\\\",\\n \\\"fwdPacketSizeTotalPerTime\\\",\\n \\\"bwdPacketSizeTotalPerTime\\\",\\n \\\"Type\\\",\\n]\";\n",
|
||
" var nbb_formatted_code = \"# Feature columns.\\nfeature_cols = [\\n \\\"flowDuration\\\",\\n \\\"flowLength\\\",\\n \\\"fwdFlowLength\\\",\\n \\\"bwdFlowLength\\\",\\n \\\"packetSizeTotal\\\",\\n \\\"packetSizeMean\\\",\\n \\\"packetSizeStd\\\",\\n \\\"packetSizeMin\\\",\\n \\\"packetSizeMax\\\",\\n \\\"fwdPacketSizeTotal\\\",\\n \\\"bwdPacketSizeTotal\\\",\\n \\\"fwdPacketSizeMean\\\",\\n \\\"bwdPacketSizeMean\\\",\\n \\\"fwdPacketSizeStd\\\",\\n \\\"bwdPacketSizeStd\\\",\\n \\\"fwdPacketSizeMin\\\",\\n \\\"bwdPacketSizeMin\\\",\\n \\\"fwdPacketSizeMax\\\",\\n \\\"bwdPacketSizeMax\\\",\\n \\\"IATMean\\\",\\n \\\"IATStd\\\",\\n \\\"IATMin\\\",\\n \\\"IATMax\\\",\\n \\\"fwdIATTotal\\\",\\n \\\"bwdIATTotal\\\",\\n \\\"fwdIATMean\\\",\\n \\\"bwdIATMean\\\",\\n \\\"fwdIATStd\\\",\\n \\\"bwdIATStd\\\",\\n \\\"fwdIATMin\\\",\\n \\\"bwdIATMin\\\",\\n \\\"fwdIATMax\\\",\\n \\\"bwdIATMax\\\",\\n \\\"flowLengthPerTime\\\",\\n \\\"fwdFlowLengthPerTime\\\",\\n \\\"bwdFlowLengthPerTime\\\",\\n \\\"packetSizeTotalPerTime\\\",\\n \\\"fwdPacketSizeTotalPerTime\\\",\\n \\\"bwdPacketSizeTotalPerTime\\\",\\n \\\"Type\\\",\\n]\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Feature columns.\n",
|
||
"feature_cols = [\n",
|
||
" \"flowDuration\",\n",
|
||
" \"flowLength\",\n",
|
||
" \"fwdFlowLength\",\n",
|
||
" \"bwdFlowLength\",\n",
|
||
" \"packetSizeTotal\",\n",
|
||
" \"packetSizeMean\",\n",
|
||
" \"packetSizeStd\",\n",
|
||
" \"packetSizeMin\",\n",
|
||
" \"packetSizeMax\",\n",
|
||
" \"fwdPacketSizeTotal\",\n",
|
||
" \"bwdPacketSizeTotal\",\n",
|
||
" \"fwdPacketSizeMean\",\n",
|
||
" \"bwdPacketSizeMean\",\n",
|
||
" \"fwdPacketSizeStd\",\n",
|
||
" \"bwdPacketSizeStd\",\n",
|
||
" \"fwdPacketSizeMin\",\n",
|
||
" \"bwdPacketSizeMin\",\n",
|
||
" \"fwdPacketSizeMax\",\n",
|
||
" \"bwdPacketSizeMax\",\n",
|
||
" \"IATMean\",\n",
|
||
" \"IATStd\",\n",
|
||
" \"IATMin\",\n",
|
||
" \"IATMax\",\n",
|
||
" \"fwdIATTotal\",\n",
|
||
" \"bwdIATTotal\",\n",
|
||
" \"fwdIATMean\",\n",
|
||
" \"bwdIATMean\",\n",
|
||
" \"fwdIATStd\",\n",
|
||
" \"bwdIATStd\",\n",
|
||
" \"fwdIATMin\",\n",
|
||
" \"bwdIATMin\",\n",
|
||
" \"fwdIATMax\",\n",
|
||
" \"bwdIATMax\",\n",
|
||
" \"flowLengthPerTime\",\n",
|
||
" \"fwdFlowLengthPerTime\",\n",
|
||
" \"bwdFlowLengthPerTime\",\n",
|
||
" \"packetSizeTotalPerTime\",\n",
|
||
" \"fwdPacketSizeTotalPerTime\",\n",
|
||
" \"bwdPacketSizeTotalPerTime\",\n",
|
||
" \"Type\",\n",
|
||
"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Dataframe with chosen features"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 10;\n",
|
||
" var nbb_unformatted_code = \"## Select feature columns in datasets.\\npd_comb_features = pd_comb[feature_cols]\";\n",
|
||
" var nbb_formatted_code = \"## Select feature columns in datasets.\\npd_comb_features = pd_comb[feature_cols]\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"## Select feature columns in datasets.\n",
|
||
"pd_comb_features = pd_comb[feature_cols]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Feature importance plots"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Density')"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 10;\n",
|
||
" var nbb_unformatted_code = \"#feature = \\\"fwdPacketSizeMax\\\"\\nfeature = \\\"fwdPacketSizeMax\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\", zorder=2\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\", zorder=3\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_formatted_code = \"# feature = \\\"fwdPacketSizeMax\\\"\\nfeature = \\\"fwdPacketSizeMax\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\",\\n zorder=2,\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\",\\n zorder=3,\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#feature = \"fwdPacketSizeMax\"\n",
|
||
"feature = \"fwdPacketSizeMax\"\n",
|
||
"\n",
|
||
"df_benign = pd_benign\n",
|
||
"df_malicious = pd_malicious\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN1, MAX1 = (\n",
|
||
" df_benign[feature].min(),\n",
|
||
" df_benign[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN2, MAX2 = (\n",
|
||
" df_malicious[feature].min(),\n",
|
||
" df_malicious[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"NUM_BINS = 50\n",
|
||
"\n",
|
||
"IF_NORM = True\n",
|
||
"\n",
|
||
"ylabel_dict = {True: \"Density\", False: \"Frequency\"}\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_benign[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN1, MAX1, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Benign\", zorder=2\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_malicious[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN2, MAX2, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Malicious\", zorder=3\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.gca().set_xscale(\"log\")\n",
|
||
"plt.gca().set_yscale(\"log\")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"plt.xlabel(feature)\n",
|
||
"plt.ylabel(ylabel_dict[IF_NORM])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Density')"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
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||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 12;\n",
|
||
" var nbb_unformatted_code = \"#feature = \\\"flowDuration\\\"\\nfeature = \\\"flowDuration\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\", zorder=2\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\", zorder=3\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_formatted_code = \"# feature = \\\"flowDuration\\\"\\nfeature = \\\"flowDuration\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\",\\n zorder=2,\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\",\\n zorder=3,\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#feature = \"flowDuration\"\n",
|
||
"feature = \"flowDuration\"\n",
|
||
"\n",
|
||
"df_benign = pd_benign\n",
|
||
"df_malicious = pd_malicious\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN1, MAX1 = (\n",
|
||
" df_benign[feature].min(),\n",
|
||
" df_benign[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN2, MAX2 = (\n",
|
||
" df_malicious[feature].min(),\n",
|
||
" df_malicious[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"NUM_BINS = 50\n",
|
||
"\n",
|
||
"IF_NORM = True\n",
|
||
"\n",
|
||
"ylabel_dict = {True: \"Density\", False: \"Frequency\"}\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_benign[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN1, MAX1, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Benign\", zorder=2\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_malicious[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN2, MAX2, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Malicious\", zorder=3\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.gca().set_xscale(\"log\")\n",
|
||
"plt.gca().set_yscale(\"log\")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"plt.xlabel(feature)\n",
|
||
"plt.ylabel(ylabel_dict[IF_NORM])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Density')"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 13;\n",
|
||
" var nbb_unformatted_code = \"#feature = \\\"fwdPacketSizeTotal\\\"\\nfeature = \\\"fwdPacketSizeTotal\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\", zorder=2\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\", zorder=3\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_formatted_code = \"# feature = \\\"fwdPacketSizeTotal\\\"\\nfeature = \\\"fwdPacketSizeTotal\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\",\\n zorder=2,\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\",\\n zorder=3,\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#feature = \"fwdPacketSizeTotal\"\n",
|
||
"feature = \"fwdPacketSizeTotal\"\n",
|
||
"\n",
|
||
"df_benign = pd_benign\n",
|
||
"df_malicious = pd_malicious\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN1, MAX1 = (\n",
|
||
" df_benign[feature].min(),\n",
|
||
" df_benign[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN2, MAX2 = (\n",
|
||
" df_malicious[feature].min(),\n",
|
||
" df_malicious[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"NUM_BINS = 50\n",
|
||
"\n",
|
||
"IF_NORM = True\n",
|
||
"\n",
|
||
"ylabel_dict = {True: \"Density\", False: \"Frequency\"}\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_benign[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN1, MAX1, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Benign\", zorder=2\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_malicious[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN2, MAX2, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Malicious\", zorder=3\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.gca().set_xscale(\"log\")\n",
|
||
"plt.gca().set_yscale(\"log\")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"plt.xlabel(feature)\n",
|
||
"plt.ylabel(ylabel_dict[IF_NORM])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Density')"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 14;\n",
|
||
" var nbb_unformatted_code = \"#feature = \\\"fwdFlowLength\\\"\\nfeature = \\\"fwdFlowLength\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\", zorder=2\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\", zorder=3\\n)\\n\\n\\n#plt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_formatted_code = \"# feature = \\\"fwdFlowLength\\\"\\nfeature = \\\"fwdFlowLength\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\",\\n zorder=2,\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\",\\n zorder=3,\\n)\\n\\n\\n# plt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#feature = \"fwdFlowLength\"\n",
|
||
"feature = \"fwdFlowLength\"\n",
|
||
"\n",
|
||
"df_benign = pd_benign\n",
|
||
"df_malicious = pd_malicious\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN1, MAX1 = (\n",
|
||
" df_benign[feature].min(),\n",
|
||
" df_benign[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN2, MAX2 = (\n",
|
||
" df_malicious[feature].min(),\n",
|
||
" df_malicious[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"NUM_BINS = 50\n",
|
||
"\n",
|
||
"IF_NORM = True\n",
|
||
"\n",
|
||
"ylabel_dict = {True: \"Density\", False: \"Frequency\"}\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_benign[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN1, MAX1, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Benign\", zorder=2\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_malicious[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN2, MAX2, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Malicious\", zorder=3\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"#plt.gca().set_xscale(\"log\")\n",
|
||
"plt.gca().set_yscale(\"log\")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"plt.xlabel(feature)\n",
|
||
"plt.ylabel(ylabel_dict[IF_NORM])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Density')"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 15;\n",
|
||
" var nbb_unformatted_code = \"#feature = \\\"fwdIATMean\\\"\\nfeature = \\\"fwdIATMean\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\", zorder=2\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\", zorder=3\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_formatted_code = \"# feature = \\\"fwdIATMean\\\"\\nfeature = \\\"fwdIATMean\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\",\\n zorder=2,\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\",\\n zorder=3,\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#feature = \"fwdIATMean\"\n",
|
||
"feature = \"fwdIATMean\"\n",
|
||
"\n",
|
||
"df_benign = pd_benign\n",
|
||
"df_malicious = pd_malicious\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN1, MAX1 = (\n",
|
||
" df_benign[feature].min(),\n",
|
||
" df_benign[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN2, MAX2 = (\n",
|
||
" df_malicious[feature].min(),\n",
|
||
" df_malicious[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"NUM_BINS = 50\n",
|
||
"\n",
|
||
"IF_NORM = True\n",
|
||
"\n",
|
||
"ylabel_dict = {True: \"Density\", False: \"Frequency\"}\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_benign[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN1, MAX1, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Benign\", zorder=2\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_malicious[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN2, MAX2, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Malicious\", zorder=3\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.gca().set_xscale(\"log\")\n",
|
||
"plt.gca().set_yscale(\"log\")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"plt.xlabel(feature)\n",
|
||
"plt.ylabel(ylabel_dict[IF_NORM])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Density')"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 16;\n",
|
||
" var nbb_unformatted_code = \"#feature = \\\"bwdIATMean\\\"\\nfeature = \\\"bwdIATMean\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\", zorder=2\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\", zorder=3\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_formatted_code = \"# feature = \\\"bwdIATMean\\\"\\nfeature = \\\"bwdIATMean\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\",\\n zorder=2,\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\",\\n zorder=3,\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#feature = \"bwdIATMean\"\n",
|
||
"feature = \"bwdIATMean\"\n",
|
||
"\n",
|
||
"df_benign = pd_benign\n",
|
||
"df_malicious = pd_malicious\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN1, MAX1 = (\n",
|
||
" df_benign[feature].min(),\n",
|
||
" df_benign[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN2, MAX2 = (\n",
|
||
" df_malicious[feature].min(),\n",
|
||
" df_malicious[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"NUM_BINS = 50\n",
|
||
"\n",
|
||
"IF_NORM = True\n",
|
||
"\n",
|
||
"ylabel_dict = {True: \"Density\", False: \"Frequency\"}\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_benign[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN1, MAX1, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Benign\", zorder=2\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_malicious[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN2, MAX2, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Malicious\", zorder=3\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.gca().set_xscale(\"log\")\n",
|
||
"plt.gca().set_yscale(\"log\")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"plt.xlabel(feature)\n",
|
||
"plt.ylabel(ylabel_dict[IF_NORM])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Density')"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 17;\n",
|
||
" var nbb_unformatted_code = \"#feature = \\\"fwdPacketSizeMean\\\"\\nfeature = \\\"fwdPacketSizeMean\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\", zorder=2\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\", zorder=3\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_formatted_code = \"# feature = \\\"fwdPacketSizeMean\\\"\\nfeature = \\\"fwdPacketSizeMean\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\",\\n zorder=2,\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\",\\n zorder=3,\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#feature = \"fwdPacketSizeMean\"\n",
|
||
"feature = \"fwdPacketSizeMean\"\n",
|
||
"\n",
|
||
"df_benign = pd_benign\n",
|
||
"df_malicious = pd_malicious\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN1, MAX1 = (\n",
|
||
" df_benign[feature].min(),\n",
|
||
" df_benign[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN2, MAX2 = (\n",
|
||
" df_malicious[feature].min(),\n",
|
||
" df_malicious[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"NUM_BINS = 50\n",
|
||
"\n",
|
||
"IF_NORM = True\n",
|
||
"\n",
|
||
"ylabel_dict = {True: \"Density\", False: \"Frequency\"}\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_benign[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN1, MAX1, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Benign\", zorder=2\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_malicious[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN2, MAX2, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Malicious\", zorder=3\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.gca().set_xscale(\"log\")\n",
|
||
"plt.gca().set_yscale(\"log\")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"plt.xlabel(feature)\n",
|
||
"plt.ylabel(ylabel_dict[IF_NORM])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Density')"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 18;\n",
|
||
" var nbb_unformatted_code = \"#feature = \\\"packetSizeMax\\\"\\nfeature = \\\"packetSizeMax\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\", zorder=2\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\", zorder=3\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_formatted_code = \"# feature = \\\"packetSizeMax\\\"\\nfeature = \\\"packetSizeMax\\\"\\n\\ndf_benign = pd_benign\\ndf_malicious = pd_malicious\\n\\n\\nMIN1, MAX1 = (\\n df_benign[feature].min(),\\n df_benign[feature].max(),\\n)\\n\\n\\nMIN2, MAX2 = (\\n df_malicious[feature].min(),\\n df_malicious[feature].max(),\\n)\\n\\n\\nNUM_BINS = 50\\n\\nIF_NORM = True\\n\\nylabel_dict = {True: \\\"Density\\\", False: \\\"Frequency\\\"}\\n\\nplt.hist(\\n x=df_benign[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN1, MAX1, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Benign\\\",\\n zorder=2,\\n)\\n\\nplt.hist(\\n x=df_malicious[feature].values,\\n # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\\n bins=np.linspace(MIN2, MAX2, NUM_BINS),\\n density=IF_NORM,\\n label=\\\"Malicious\\\",\\n zorder=3,\\n)\\n\\n\\nplt.gca().set_xscale(\\\"log\\\")\\nplt.gca().set_yscale(\\\"log\\\")\\n\\nplt.legend()\\nplt.xlabel(feature)\\nplt.ylabel(ylabel_dict[IF_NORM])\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"#feature = \"packetSizeMax\"\n",
|
||
"feature = \"packetSizeMax\"\n",
|
||
"\n",
|
||
"df_benign = pd_benign\n",
|
||
"df_malicious = pd_malicious\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN1, MAX1 = (\n",
|
||
" df_benign[feature].min(),\n",
|
||
" df_benign[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"MIN2, MAX2 = (\n",
|
||
" df_malicious[feature].min(),\n",
|
||
" df_malicious[feature].max(),\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"NUM_BINS = 50\n",
|
||
"\n",
|
||
"IF_NORM = True\n",
|
||
"\n",
|
||
"ylabel_dict = {True: \"Density\", False: \"Frequency\"}\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_benign[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN1, MAX1, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Benign\", zorder=2\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.hist(\n",
|
||
" x=df_malicious[feature].values,\n",
|
||
" # bins=10 ** np.linspace(np.log10(MIN1), np.log10(MAX1), NUM_BINS),\n",
|
||
" bins=np.linspace(MIN2, MAX2, NUM_BINS),\n",
|
||
" density=IF_NORM,\n",
|
||
" label=\"Malicious\", zorder=3\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.gca().set_xscale(\"log\")\n",
|
||
"plt.gca().set_yscale(\"log\")\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"plt.xlabel(feature)\n",
|
||
"plt.ylabel(ylabel_dict[IF_NORM])\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Machine learning - feature importance"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Clean dataset"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 11;\n",
|
||
" var nbb_unformatted_code = \"# Remove spurious entries from dataset.\\ndef clean_dataset(df):\\n assert isinstance(df, pd.DataFrame), \\\"df needs to be a pd.DataFrame\\\"\\n df.dropna(inplace=True)\\n\\n df_X = df.iloc[:, :-1]\\n df_Y = df.iloc[:, -1]\\n\\n print(df.shape, df_X.shape, df_Y.shape)\\n indices_to_keep = ~df_X.isin([np.nan, np.inf, -np.inf]).any(1)\\n return df_X[indices_to_keep].astype(np.float64).values, df_Y[indices_to_keep].values\";\n",
|
||
" var nbb_formatted_code = \"# Remove spurious entries from dataset.\\ndef clean_dataset(df):\\n assert isinstance(df, pd.DataFrame), \\\"df needs to be a pd.DataFrame\\\"\\n df.dropna(inplace=True)\\n\\n df_X = df.iloc[:, :-1]\\n df_Y = df.iloc[:, -1]\\n\\n print(df.shape, df_X.shape, df_Y.shape)\\n indices_to_keep = ~df_X.isin([np.nan, np.inf, -np.inf]).any(1)\\n return df_X[indices_to_keep].astype(np.float64).values, df_Y[indices_to_keep].values\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Remove spurious entries from dataset.\n",
|
||
"def clean_dataset(df):\n",
|
||
" assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
|
||
" df.dropna(inplace=True)\n",
|
||
"\n",
|
||
" df_X = df.iloc[:, :-1]\n",
|
||
" df_Y = df.iloc[:, -1]\n",
|
||
"\n",
|
||
" print(df.shape, df_X.shape, df_Y.shape)\n",
|
||
" indices_to_keep = ~df_X.isin([np.nan, np.inf, -np.inf]).any(1)\n",
|
||
" return df_X[indices_to_keep].astype(np.float64).values, df_Y[indices_to_keep].values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Get feature and class arrays (X and y.)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(28962, 40) (28962, 39) (28962,)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 12;\n",
|
||
" var nbb_unformatted_code = \"# Get feature and class arrays (X and y.)\\npd_comb_features_cp = pd_comb_features.copy(deep=True)\\n\\nX, y = clean_dataset(pd_comb_features_cp)\";\n",
|
||
" var nbb_formatted_code = \"# Get feature and class arrays (X and y.)\\npd_comb_features_cp = pd_comb_features.copy(deep=True)\\n\\nX, y = clean_dataset(pd_comb_features_cp)\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Get feature and class arrays (X and y.)\n",
|
||
"pd_comb_features_cp = pd_comb_features.copy(deep=True)\n",
|
||
"\n",
|
||
"X, y = clean_dataset(pd_comb_features_cp)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Train test split"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(20273, 39) (8689, 39)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 13;\n",
|
||
" var nbb_unformatted_code = \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\\n\\nprint(X_train.shape, X_test.shape)\";\n",
|
||
" var nbb_formatted_code = \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\\n\\nprint(X_train.shape, X_test.shape)\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
|
||
"\n",
|
||
"print(X_train.shape, X_test.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Scale data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 14;\n",
|
||
" var nbb_unformatted_code = \"scaler = StandardScaler() # MinMaxScaler\\nX_train_scale = scaler.fit_transform(X_train)\\nX_test_scale = scaler.transform(X_test)\";\n",
|
||
" var nbb_formatted_code = \"scaler = StandardScaler() # MinMaxScaler\\nX_train_scale = scaler.fit_transform(X_train)\\nX_test_scale = scaler.transform(X_test)\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"scaler = StandardScaler() # MinMaxScaler\n",
|
||
"X_train_scale = scaler.fit_transform(X_train)\n",
|
||
"X_test_scale = scaler.transform(X_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Weighted Logistic Regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Hyperparameter grid search."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 15;\n",
|
||
" var nbb_unformatted_code = \"# Class weights.\\nw = [\\n {0: 0.10, 1: 99.90},\\n {0: 0.25, 1: 99.75},\\n {0: 0.50, 1: 99.50},\\n {0: 0.75, 1: 99.25},\\n {0: 1.00, 1: 99.00},\\n {\\n 0: 100\\n * np.sum(y == \\\"Malicious\\\")\\n / (np.sum(y == \\\"Benign\\\") + np.sum(y == \\\"Malicious\\\")),\\n 1: 100\\n * np.sum(y == \\\"Benign\\\")\\n / (np.sum(y == \\\"Benign\\\") + np.sum(y == \\\"Malicious\\\")),\\n },\\n]\\n\\n# Inverse of regularization strength.\\ncrange = np.arange(0.1, 1.0, 0.2)\\n\\n\\n# Hyperparameter grid.\\nhyperparam_grid = {\\n \\\"class_weight\\\": w,\\n \\\"penalty\\\": [\\\"l1\\\", \\\"l2\\\"],\\n \\\"C\\\": crange,\\n \\\"fit_intercept\\\": [True, False],\\n}\";\n",
|
||
" var nbb_formatted_code = \"# Class weights.\\nw = [\\n {0: 0.10, 1: 99.90},\\n {0: 0.25, 1: 99.75},\\n {0: 0.50, 1: 99.50},\\n {0: 0.75, 1: 99.25},\\n {0: 1.00, 1: 99.00},\\n {\\n 0: 100\\n * np.sum(y == \\\"Malicious\\\")\\n / (np.sum(y == \\\"Benign\\\") + np.sum(y == \\\"Malicious\\\")),\\n 1: 100\\n * np.sum(y == \\\"Benign\\\")\\n / (np.sum(y == \\\"Benign\\\") + np.sum(y == \\\"Malicious\\\")),\\n },\\n]\\n\\n# Inverse of regularization strength.\\ncrange = np.arange(0.1, 1.0, 0.2)\\n\\n\\n# Hyperparameter grid.\\nhyperparam_grid = {\\n \\\"class_weight\\\": w,\\n \\\"penalty\\\": [\\\"l1\\\", \\\"l2\\\"],\\n \\\"C\\\": crange,\\n \\\"fit_intercept\\\": [True, False],\\n}\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Class weights.\n",
|
||
"w = [\n",
|
||
" {0: 0.10, 1: 99.90},\n",
|
||
" {0: 0.25, 1: 99.75},\n",
|
||
" {0: 0.50, 1: 99.50},\n",
|
||
" {0: 0.75, 1: 99.25},\n",
|
||
" {0: 1.00, 1: 99.00},\n",
|
||
" {\n",
|
||
" 0: 100\n",
|
||
" * np.sum(y == \"Malicious\")\n",
|
||
" / (np.sum(y == \"Benign\") + np.sum(y == \"Malicious\")),\n",
|
||
" 1: 100\n",
|
||
" * np.sum(y == \"Benign\")\n",
|
||
" / (np.sum(y == \"Benign\") + np.sum(y == \"Malicious\")),\n",
|
||
" },\n",
|
||
"]\n",
|
||
"\n",
|
||
"# Inverse of regularization strength.\n",
|
||
"crange = np.arange(0.1, 1.0, 0.2)\n",
|
||
"\n",
|
||
"\n",
|
||
"# Hyperparameter grid.\n",
|
||
"hyperparam_grid = {\n",
|
||
" \"class_weight\": w,\n",
|
||
" \"penalty\": [\"l1\", \"l2\"],\n",
|
||
" \"C\": crange,\n",
|
||
" \"fit_intercept\": [True, False],\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Prepare samples."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 16;\n",
|
||
" var nbb_unformatted_code = \"# String to int for classes\\ny_train2 = np.copy(y_train)\\ny_train2[np.where(y_train == \\\"Benign\\\")[0]] = 0\\ny_train2[np.where(y_train == \\\"Malicious\\\")[0]] = 1\";\n",
|
||
" var nbb_formatted_code = \"# String to int for classes\\ny_train2 = np.copy(y_train)\\ny_train2[np.where(y_train == \\\"Benign\\\")[0]] = 0\\ny_train2[np.where(y_train == \\\"Malicious\\\")[0]] = 1\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# String to int for classes\n",
|
||
"y_train2 = np.copy(y_train)\n",
|
||
"y_train2[np.where(y_train == \"Benign\")[0]] = 0\n",
|
||
"y_train2[np.where(y_train == \"Malicious\")[0]] = 1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Model fitting."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/siddharthsatpathy/opt/anaconda3/lib/python3.8/site-packages/scikit_learn-1.0.dev0-py3.8-macosx-10.9-x86_64.egg/sklearn/model_selection/_search.py:890: UserWarning: One or more of the test scores are non-finite: [ nan 0.9880426 nan 0.98095783 nan 0.99057238\n",
|
||
" nan 0.97893785 nan 0.99171251 nan 0.97658718\n",
|
||
" nan 0.99194833 nan 0.97491777 nan 0.99202053\n",
|
||
" nan 0.97368655 nan 0.99198866 nan 0.97435697\n",
|
||
" nan 0.98991559 nan 0.98147736 nan 0.99206716\n",
|
||
" nan 0.97907415 nan 0.99294926 nan 0.97651238\n",
|
||
" nan 0.99310424 nan 0.97479531 nan 0.99329743\n",
|
||
" nan 0.97358621 nan 0.99321499 nan 0.97423402\n",
|
||
" nan 0.9905359 nan 0.98155072 nan 0.99265558\n",
|
||
" nan 0.97911204 nan 0.99344045 nan 0.97649611\n",
|
||
" nan 0.99376651 nan 0.97475562 nan 0.99400771\n",
|
||
" nan 0.97355775 nan 0.99385109 nan 0.97419971\n",
|
||
" nan 0.99099631 nan 0.98158927 nan 0.99312902\n",
|
||
" nan 0.97911352 nan 0.99379647 nan 0.97649009\n",
|
||
" nan 0.99421899 nan 0.97475513 nan 0.99447011\n",
|
||
" nan 0.97355238 nan 0.99435966 nan 0.97419141\n",
|
||
" nan 0.99141737 nan 0.98158634 nan 0.99345803\n",
|
||
" nan 0.97914409 nan 0.99406938 nan 0.9764844\n",
|
||
" nan 0.99454865 nan 0.97473854 nan 0.99480042\n",
|
||
" nan 0.97354408 nan 0.99466787 nan 0.97418263]\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Best score: 0.9948004205120142 with param: {'C': 0.9000000000000001, 'class_weight': {0: 1.0, 1: 99.0}, 'fit_intercept': True, 'penalty': 'l2'}\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/siddharthsatpathy/opt/anaconda3/lib/python3.8/site-packages/scikit_learn-1.0.dev0-py3.8-macosx-10.9-x86_64.egg/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation for alternative solver options:\n",
|
||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||
" n_iter_i = _check_optimize_result(\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 17;\n",
|
||
" var nbb_unformatted_code = \"# logistic model classifier.\\nlg = LogisticRegression(random_state=13)\\n\\n# define evaluation procedure\\ngrid = GridSearchCV(\\n lg, hyperparam_grid, scoring=\\\"roc_auc\\\", cv=10, n_jobs=-1, refit=True\\n)\\ngrid.fit(X_train_scale, y_train2.astype(\\\"int32\\\"))\\n\\nprint(f\\\"Best score: {grid.best_score_} with param: {grid.best_params_}\\\")\";\n",
|
||
" var nbb_formatted_code = \"# logistic model classifier.\\nlg = LogisticRegression(random_state=13)\\n\\n# define evaluation procedure\\ngrid = GridSearchCV(\\n lg, hyperparam_grid, scoring=\\\"roc_auc\\\", cv=10, n_jobs=-1, refit=True\\n)\\ngrid.fit(X_train_scale, y_train2.astype(\\\"int32\\\"))\\n\\nprint(f\\\"Best score: {grid.best_score_} with param: {grid.best_params_}\\\")\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# logistic model classifier.\n",
|
||
"lg = LogisticRegression(random_state=13)\n",
|
||
"\n",
|
||
"# define evaluation procedure\n",
|
||
"grid = GridSearchCV(\n",
|
||
" lg, hyperparam_grid, scoring=\"roc_auc\", cv=10, n_jobs=-1, refit=True\n",
|
||
")\n",
|
||
"grid.fit(X_train_scale, y_train2.astype(\"int32\"))\n",
|
||
"\n",
|
||
"print(f\"Best score: {grid.best_score_} with param: {grid.best_params_}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Test perfomance."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Accuracy Score: 0.9766371274024629\n",
|
||
"Confusion Matrix: \n",
|
||
"[[8418 202]\n",
|
||
" [ 1 68]]\n",
|
||
"Area Under Curve: 0.9810366858334173\n",
|
||
"Recall score (Pct of true malicious detected): 98.55072463768117\n",
|
||
"Data reduction: 3.11 percent\n",
|
||
"Pct malicious in data sent to console: 25.19 percent\n",
|
||
"F1 score: 0.9834254890707712\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 18;\n",
|
||
" var nbb_unformatted_code = \"# test\\ny_pred_wt = grid.predict(X_test_scale)\\n\\ny_test2 = np.copy(y_test)\\ny_test2[np.where(y_test == \\\"Benign\\\")[0]] = 0\\ny_test2[np.where(y_test == \\\"Malicious\\\")[0]] = 1\\n\\n\\n# performance\\nconf_mat = confusion_matrix(y_test2.astype(\\\"int32\\\"), y_pred_wt)\\n\\nprint(f\\\"Accuracy Score: {accuracy_score(y_test2.astype('int32'),y_pred_wt)}\\\")\\nprint(f\\\"Confusion Matrix: \\\\n{confusion_matrix(y_test2.astype('int32'), y_pred_wt)}\\\")\\nprint(f\\\"Area Under Curve: {roc_auc_score(y_test2.astype('int32'), y_pred_wt)}\\\")\\nprint(\\n f\\\"Recall score (Pct of true malicious detected): {100*recall_score(y_test2.astype('int32'), y_pred_wt)}\\\"\\n)\\nprint(\\n f\\\"Data reduction: { np.round( 100.0 * conf_mat.T[1].sum() / conf_mat.sum() , 2 )} percent\\\"\\n)\\n\\nprint(\\n f\\\"Pct malicious in data sent to console: { np.round( 100.0 * conf_mat.T[1][1] / conf_mat.T[1].sum() , 2 )} percent\\\"\\n)\\n\\nprint(\\\"F1 score: \\\", f1_score(y_test2.astype(\\\"int32\\\"), y_pred_wt, average=\\\"weighted\\\"))\";\n",
|
||
" var nbb_formatted_code = \"# test\\ny_pred_wt = grid.predict(X_test_scale)\\n\\ny_test2 = np.copy(y_test)\\ny_test2[np.where(y_test == \\\"Benign\\\")[0]] = 0\\ny_test2[np.where(y_test == \\\"Malicious\\\")[0]] = 1\\n\\n\\n# performance\\nconf_mat = confusion_matrix(y_test2.astype(\\\"int32\\\"), y_pred_wt)\\n\\nprint(f\\\"Accuracy Score: {accuracy_score(y_test2.astype('int32'),y_pred_wt)}\\\")\\nprint(f\\\"Confusion Matrix: \\\\n{confusion_matrix(y_test2.astype('int32'), y_pred_wt)}\\\")\\nprint(f\\\"Area Under Curve: {roc_auc_score(y_test2.astype('int32'), y_pred_wt)}\\\")\\nprint(\\n f\\\"Recall score (Pct of true malicious detected): {100*recall_score(y_test2.astype('int32'), y_pred_wt)}\\\"\\n)\\nprint(\\n f\\\"Data reduction: { np.round( 100.0 * conf_mat.T[1].sum() / conf_mat.sum() , 2 )} percent\\\"\\n)\\n\\nprint(\\n f\\\"Pct malicious in data sent to console: { np.round( 100.0 * conf_mat.T[1][1] / conf_mat.T[1].sum() , 2 )} percent\\\"\\n)\\n\\nprint(\\\"F1 score: \\\", f1_score(y_test2.astype(\\\"int32\\\"), y_pred_wt, average=\\\"weighted\\\"))\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# test\n",
|
||
"y_pred_wt = grid.predict(X_test_scale)\n",
|
||
"\n",
|
||
"y_test2 = np.copy(y_test)\n",
|
||
"y_test2[np.where(y_test == \"Benign\")[0]] = 0\n",
|
||
"y_test2[np.where(y_test == \"Malicious\")[0]] = 1\n",
|
||
"\n",
|
||
"\n",
|
||
"# performance\n",
|
||
"conf_mat = confusion_matrix(y_test2.astype(\"int32\"), y_pred_wt)\n",
|
||
"\n",
|
||
"print(f\"Accuracy Score: {accuracy_score(y_test2.astype('int32'),y_pred_wt)}\")\n",
|
||
"print(f\"Confusion Matrix: \\n{confusion_matrix(y_test2.astype('int32'), y_pred_wt)}\")\n",
|
||
"print(f\"Area Under Curve: {roc_auc_score(y_test2.astype('int32'), y_pred_wt)}\")\n",
|
||
"print(\n",
|
||
" f\"Recall score (Pct of true malicious detected): {100*recall_score(y_test2.astype('int32'), y_pred_wt)}\"\n",
|
||
")\n",
|
||
"print(\n",
|
||
" f\"Data reduction: { np.round( 100.0 * conf_mat.T[1].sum() / conf_mat.sum() , 2 )} percent\"\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\n",
|
||
" f\"Pct malicious in data sent to console: { np.round( 100.0 * conf_mat.T[1][1] / conf_mat.T[1].sum() , 2 )} percent\"\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"F1 score: \", f1_score(y_test2.astype(\"int32\"), y_pred_wt, average=\"weighted\"))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Visualization of results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"code_folding": [
|
||
0
|
||
]
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 19;\n",
|
||
" var nbb_unformatted_code = \"# Confusion matrix\\nxarray = [\\\"Benign\\\", \\\"Malicious\\\"]\\nyarray = [\\\"Benign\\\", \\\"Malicious\\\"]\\n\\nconf_matrix = confusion_matrix(y_test2.astype(\\\"int32\\\"), y_pred_wt)\\n\\n\\nfig, ax = plt.subplots()\\nim = ax.imshow(conf_matrix, cmap=plt.cm.jet, clim=(0, 1000))\\n\\n# Show all ticks and label them with the respective list entries\\nax.set_xticks(np.arange(len(xarray)))\\nax.set_xticklabels(xarray)\\n\\nax.set_yticks(np.arange(len(yarray)))\\nax.set_yticklabels(yarray)\\n\\n# Rotate the tick labels and set their alignment.\\nplt.setp(ax.get_xticklabels(), rotation=45, ha=\\\"right\\\", rotation_mode=\\\"anchor\\\")\\n\\n# Loop over data dimensions and create text annotations.\\nfor i in range(len(xarray)):\\n for j in range(len(yarray)):\\n text = ax.text(j, i, conf_matrix[i, j], ha=\\\"center\\\", va=\\\"center\\\", color=\\\"w\\\")\\n\\nax.set_title(\\\"Confusion matrix\\\")\\nfig.tight_layout()\\nplt.show()\";\n",
|
||
" var nbb_formatted_code = \"# Confusion matrix\\nxarray = [\\\"Benign\\\", \\\"Malicious\\\"]\\nyarray = [\\\"Benign\\\", \\\"Malicious\\\"]\\n\\nconf_matrix = confusion_matrix(y_test2.astype(\\\"int32\\\"), y_pred_wt)\\n\\n\\nfig, ax = plt.subplots()\\nim = ax.imshow(conf_matrix, cmap=plt.cm.jet, clim=(0, 1000))\\n\\n# Show all ticks and label them with the respective list entries\\nax.set_xticks(np.arange(len(xarray)))\\nax.set_xticklabels(xarray)\\n\\nax.set_yticks(np.arange(len(yarray)))\\nax.set_yticklabels(yarray)\\n\\n# Rotate the tick labels and set their alignment.\\nplt.setp(ax.get_xticklabels(), rotation=45, ha=\\\"right\\\", rotation_mode=\\\"anchor\\\")\\n\\n# Loop over data dimensions and create text annotations.\\nfor i in range(len(xarray)):\\n for j in range(len(yarray)):\\n text = ax.text(j, i, conf_matrix[i, j], ha=\\\"center\\\", va=\\\"center\\\", color=\\\"w\\\")\\n\\nax.set_title(\\\"Confusion matrix\\\")\\nfig.tight_layout()\\nplt.show()\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Confusion matrix\n",
|
||
"xarray = [\"Benign\", \"Malicious\"]\n",
|
||
"yarray = [\"Benign\", \"Malicious\"]\n",
|
||
"\n",
|
||
"conf_matrix = confusion_matrix(y_test2.astype(\"int32\"), y_pred_wt)\n",
|
||
"\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots()\n",
|
||
"im = ax.imshow(conf_matrix, cmap=plt.cm.jet, clim=(0, 1000))\n",
|
||
"\n",
|
||
"# Show all ticks and label them with the respective list entries\n",
|
||
"ax.set_xticks(np.arange(len(xarray)))\n",
|
||
"ax.set_xticklabels(xarray)\n",
|
||
"\n",
|
||
"ax.set_yticks(np.arange(len(yarray)))\n",
|
||
"ax.set_yticklabels(yarray)\n",
|
||
"\n",
|
||
"# Rotate the tick labels and set their alignment.\n",
|
||
"plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n",
|
||
"\n",
|
||
"# Loop over data dimensions and create text annotations.\n",
|
||
"for i in range(len(xarray)):\n",
|
||
" for j in range(len(yarray)):\n",
|
||
" text = ax.text(j, i, conf_matrix[i, j], ha=\"center\", va=\"center\", color=\"w\")\n",
|
||
"\n",
|
||
"ax.set_title(\"Confusion matrix\")\n",
|
||
"fig.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Best fit parameters."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/siddharthsatpathy/opt/anaconda3/lib/python3.8/site-packages/scikit_learn-1.0.dev0-py3.8-macosx-10.9-x86_64.egg/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation for alternative solver options:\n",
|
||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||
" n_iter_i = _check_optimize_result(\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"LogisticRegression(C=0.9000000000000001, class_weight={0: 1.0, 1: 99.0},\n",
|
||
" max_iter=5, random_state=13)"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 20;\n",
|
||
" var nbb_unformatted_code = \"# define model\\nbest_fit_model = LogisticRegression(\\n class_weight=grid.best_params_[\\\"class_weight\\\"],\\n penalty=grid.best_params_[\\\"penalty\\\"],\\n C=grid.best_params_[\\\"C\\\"],\\n fit_intercept=grid.best_params_[\\\"fit_intercept\\\"],\\n random_state=13,\\n max_iter=5,\\n)\\n\\n# fit it\\nbest_fit_model.fit(X_train_scale, y_train2.astype(\\\"int32\\\"))\";\n",
|
||
" var nbb_formatted_code = \"# define model\\nbest_fit_model = LogisticRegression(\\n class_weight=grid.best_params_[\\\"class_weight\\\"],\\n penalty=grid.best_params_[\\\"penalty\\\"],\\n C=grid.best_params_[\\\"C\\\"],\\n fit_intercept=grid.best_params_[\\\"fit_intercept\\\"],\\n random_state=13,\\n max_iter=5,\\n)\\n\\n# fit it\\nbest_fit_model.fit(X_train_scale, y_train2.astype(\\\"int32\\\"))\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# define model\n",
|
||
"best_fit_model = LogisticRegression(\n",
|
||
" class_weight=grid.best_params_[\"class_weight\"],\n",
|
||
" penalty=grid.best_params_[\"penalty\"],\n",
|
||
" C=grid.best_params_[\"C\"],\n",
|
||
" fit_intercept=grid.best_params_[\"fit_intercept\"],\n",
|
||
" random_state=13,\n",
|
||
" max_iter=5,\n",
|
||
")\n",
|
||
"\n",
|
||
"# fit it\n",
|
||
"best_fit_model.fit(X_train_scale, y_train2.astype(\"int32\"))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Save parameters."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 30;\n",
|
||
" var nbb_unformatted_code = \"np.savetxt(\\\"mean.txt\\\", scaler.mean_, delimiter=\\\",\\\")\\nnp.savetxt(\\\"std.txt\\\", scaler.scale_, delimiter=\\\",\\\")\\nnp.savetxt(\\\"weights.txt\\\", best_fit_model.coef_[0], delimiter=\\\",\\\")\\nnp.savetxt(\\\"intercepts.txt\\\", best_fit_model.intercept_, delimiter=\\\",\\\")\";\n",
|
||
" var nbb_formatted_code = \"np.savetxt(\\\"mean.txt\\\", scaler.mean_, delimiter=\\\",\\\")\\nnp.savetxt(\\\"std.txt\\\", scaler.scale_, delimiter=\\\",\\\")\\nnp.savetxt(\\\"weights.txt\\\", best_fit_model.coef_[0], delimiter=\\\",\\\")\\nnp.savetxt(\\\"intercepts.txt\\\", best_fit_model.intercept_, delimiter=\\\",\\\")\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"np.savetxt(\"mean.txt\", scaler.mean_, delimiter=\",\")\n",
|
||
"np.savetxt(\"std.txt\", scaler.scale_, delimiter=\",\")\n",
|
||
"np.savetxt(\"weights.txt\", best_fit_model.coef_[0], delimiter=\",\")\n",
|
||
"np.savetxt(\"intercept.txt\", best_fit_model.intercept_, delimiter=\",\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Feature importance scores"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 21;\n",
|
||
" var nbb_unformatted_code = \"important_features = pd_comb_features_cp.iloc[:, :-1].columns.values[\\n np.argsort(-1 * np.abs(best_fit_model.coef_[0]))\\n]\";\n",
|
||
" var nbb_formatted_code = \"important_features = pd_comb_features_cp.iloc[:, :-1].columns.values[\\n np.argsort(-1 * np.abs(best_fit_model.coef_[0]))\\n]\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
||
" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"important_features = pd_comb_features_cp.iloc[:, :-1].columns.values[\n",
|
||
" np.argsort(-1 * np.abs(best_fit_model.coef_[0]))\n",
|
||
"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array(['flowLength', 'bwdFlowLength', 'packetSizeMin', 'bwdPacketSizeMin',\n",
|
||
" 'bwdPacketSizeTotalPerTime', 'packetSizeTotalPerTime',\n",
|
||
" 'packetSizeMax', 'bwdPacketSizeMax', 'bwdFlowLengthPerTime',\n",
|
||
" 'flowLengthPerTime', 'bwdIATTotal', 'flowDuration', 'IATMean',\n",
|
||
" 'bwdIATMean', 'packetSizeTotal', 'bwdPacketSizeTotal', 'bwdIATStd',\n",
|
||
" 'IATStd', 'bwdPacketSizeStd', 'packetSizeStd', 'bwdIATMax',\n",
|
||
" 'IATMax', 'packetSizeMean', 'fwdPacketSizeMean',\n",
|
||
" 'bwdPacketSizeMean', 'IATMin', 'bwdIATMin',\n",
|
||
" 'fwdPacketSizeTotalPerTime', 'fwdIATStd', 'fwdPacketSizeTotal',\n",
|
||
" 'fwdIATMin', 'fwdIATMax', 'fwdPacketSizeMax', 'fwdPacketSizeMin',\n",
|
||
" 'fwdFlowLengthPerTime', 'fwdPacketSizeStd', 'fwdFlowLength',\n",
|
||
" 'fwdIATTotal', 'fwdIATMean'], dtype=object)"
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/javascript": [
|
||
"\n",
|
||
" setTimeout(function() {\n",
|
||
" var nbb_cell_id = 22;\n",
|
||
" var nbb_unformatted_code = \"important_features\";\n",
|
||
" var nbb_formatted_code = \"important_features\";\n",
|
||
" var nbb_cells = Jupyter.notebook.get_cells();\n",
|
||
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
|
||
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
|
||
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
|
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" nbb_cells[i].set_text(nbb_formatted_code);\n",
|
||
" }\n",
|
||
" break;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 500);\n",
|
||
" "
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.Javascript object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"important_features"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.8.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|