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+{
+ "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"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}