{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "299dec31",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sdv.metadata import SingleTableMetadata\n",
"from sdmetrics.reports.single_table import QualityReport\n",
"from sdmetrics.reports.single_table import DiagnosticReport\n",
"from table_evaluator import TableEvaluator\n",
"import matplotlib.pyplot as plt\n",
"from sdmetrics.single_column import StatisticSimilarity\n",
"import math\n",
"from sdmetrics.single_column import RangeCoverage\n",
"from sdmetrics.visualization import get_column_plot\n",
"import os\n",
"import plotly.io as py\n",
"import string\n",
"\n",
"from synthcity.plugins import Plugins\n",
"\n",
"#Plugins(categories=[\"generic\", \"privacy\"]).list() #uncomment to see a list of model for generating data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6127a704",
"metadata": {},
"outputs": [],
"source": [
"#loading the preprocessed datasets \n",
"\n",
"# real_data = pd.read_csv('Datasets/Preprocessed_Datasets/benign.csv')\n",
"# real_data = pd.read_csv('Datasets/Preprocessed_Datasets/bot_attacks.csv')\n",
"# real_data = pd.read_csv('Datasets/Preprocessed_Datasets/bruteforce_attacks.csv')\n",
"# real_data = pd.read_csv('Datasets/Preprocessed_Datasets/doS_attacks.csv')\n",
"# real_data = pd.read_csv('Datasets/Preprocessed_Datasets/infilteration_attacks.csv')\n",
"\n",
"print(real_data.shape)\n",
"print(real_data.Label.unique())\n",
"\n",
"# if bruteforce_attack or dos_attacks are used then uncomment the below line and change the name of the dataset accordingly\n",
"#real_data=real_data[real_data.Label=='SSH-Bruteforce'] # change according to the dataset\n",
"real_data = real_data.iloc[:300000, :]\n",
"print(real_data.shape) # "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c41b506-aa5c-419c-8a49-f246de3ee6ae",
"metadata": {},
"outputs": [],
"source": [
"# imported from https://github.com/vanderschaarlab/synthcity/tree/main\n",
"#if using ADSGAN just change \"rtvae\" to \"adsgan\"\n",
"\n",
"syn_model = Plugins().get(\"rtvae\", n_iter= 500, lr=0.0001, batch_size= 300, decoder_n_layers_hidden=4, encoder_n_layers_hidden=4)\n",
"\n",
"syn_model.fit(real_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2510624-8cfc-480b-88ee-e2234117cb50",
"metadata": {},
"outputs": [],
"source": [
"# generating synthetic data and saving the file\n",
"synthetic_data=syn_model.generate(300000).dataframe()\n",
"synthetic_data.to_csv('RTVAE_Results/LOICHTTP.csv', index=False) #similar to the loaded dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d895ace2-e2d9-4ee0-886a-742aebcbd6c0",
"metadata": {},
"outputs": [],
"source": [
"def get_data_info(df):\n",
" \"\"\"Crates the categorical columns, continuous columns, and metadata of a dataframe.\n",
"\n",
" Args:\n",
" df (pandas.Dataframe): The input dataframe containing continuous and categorical values.\n",
"\n",
" Returns:\n",
" list: the list of categorical column names. Specifically, columns with only 4 uniques values\n",
" list: The list of continuous column names.\n",
" metadata: The metadata of the dataframe. for more informatin visit https://docs.sdv.dev/sdv/reference/metadata-spec/single-table-metadata-json\n",
" \"\"\"\n",
" #createing \n",
" categorical_columns = ['Label']\n",
" continuous_columns = []\n",
" for i in df.columns:\n",
" if i not in categorical_columns:\n",
" continuous_columns.append(i)\n",
" \n",
" #creating metadat\n",
" metadata = SingleTableMetadata()\n",
" metadata.detect_from_dataframe(df)\n",
" \n",
" for column in categorical_columns:\n",
" metadata.update_column(\n",
" column_name = column,\n",
" sdtype = 'categorical'\n",
" )\n",
" \n",
" for column in continuous_columns:\n",
" metadata.update_column(\n",
" column_name = column,\n",
" sdtype = 'numerical' \n",
" )\n",
" # validating metadata\n",
" metadata.validate()\n",
" metadata.validate_data(data=real_data)\n",
" \n",
" return categorical_columns, continuous_columns, metadata\n",
"\n",
"\n",
"categorical_columns, continuous_columns, metadata = get_data_info(real_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "90330684-4fed-4571-9026-4cb04250e475",
"metadata": {},
"outputs": [],
"source": [
"# evaluating synthetic data with table_evaluator cumulative sum per features and distribution\n",
"table_evaluator = TableEvaluator(real_data, synthetic_data, cat_cols = categorical_columns)\n",
"table_evaluator.visual_evaluation()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d8833c4f-ec42-413f-96ec-34516401ec8b",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#saving and visualizing column pair trend and column shapes\n",
"metadata = metadata.to_dict()\n",
"my_report = QualityReport()\n",
"my_report.generate(real_data, synthetic_data, metadata)\n",
"my_report.save(filepath='RTVAE_Results/LOICHTTP/quality.pkl')\n",
"my_report.get_visualization(property_name='Column Pair Trends')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a510048-8949-45a4-b9d7-543b211fc710",
"metadata": {},
"outputs": [],
"source": [
"#saving and visualiztation data validity\n",
"#metadata = metadata.to_dict()\n",
"my_report = DiagnosticReport()\n",
"my_report.generate(real_data, synthetic_data, metadata)\n",
"my_report.save(filepath='RTVAE_Results/LOICHTTP.csv/diagnostic.pkl')\n",
"#my_report.get_visualization('Data Validity')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "668f18aa-5a36-4a72-8c47-5c549a7a5a86",
"metadata": {},
"outputs": [],
"source": [
"#statistical similarity metric\n",
"sstest=[]\n",
"for i in real_data.columns:\n",
" y=StatisticSimilarity.compute(\n",
" real_data=real_data[i],\n",
" synthetic_data=synthetic_data[i],\n",
" statistic='median'\n",
" )\n",
" sstest.append(y)\n",
"\n",
"df = pd.DataFrame(sstest, columns=['SS Test'])\n",
"\n",
"print(df['SS Test'].mean())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "142c89a4-7977-49a0-8aed-69edf12ea07b",
"metadata": {},
"outputs": [],
"source": [
"#range coverage metric\n",
"range_coverage=[]\n",
"for i in real_data.columns:\n",
" \n",
" y=RangeCoverage.compute(\n",
" real_data=real_data[i],\n",
" synthetic_data=synthetic_data[i]\n",
" )\n",
" range_coverage.append(y)\n",
"df = pd.DataFrame(range_coverage, columns=['Range Coverage'])\n",
"\n",
"print(df['Range Coverage'].mean())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62ec2b27-5262-4906-a1b3-eb755c7dc0da",
"metadata": {},
"outputs": [],
"source": [
"# checking the number of unique synthetic data instances\n",
"df = pd.concat([real_data, synthetic_data], axis=0)\n",
"print(df.shape)\n",
"df.dropna(inplace=True)\n",
"df.drop_duplicates(inplace=True)\n",
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53ff9fbd-1632-4d5f-84ce-f64d13305a9b",
"metadata": {},
"outputs": [],
"source": [
"#Saving the distribution of each column\n",
"def sanitize_column_name(column_name):\n",
" valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n",
" return ''.join(c for c in column_name if c in valid_chars)\n",
"\n",
"for i in real_data.columns:\n",
" fig = get_column_plot(\n",
" real_data=real_data,\n",
" synthetic_data=synthetic_data,\n",
" column_name=i,\n",
" plot_type='bar'\n",
" )\n",
"\n",
" sanitized_column_name = sanitize_column_name(i)\n",
"\n",
" # Save the figure in the 'Pics' directory, change the location accordingly\n",
" py.write_image(fig, os.path.join('RTVAE_Results/LOICHTTP/Pics', f\"{sanitized_column_name}.png\")) \n"
]
}
],
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