diff --git a/zerotrust/Trrafic-Identify/Introducyion.pdf b/zerotrust/Trrafic-Identify/Introducyion.pdf new file mode 100644 index 0000000000000000000000000000000000000000..747456721a8592c5b1045cf1ee799bd548810551 Binary files /dev/null and b/zerotrust/Trrafic-Identify/Introducyion.pdf differ diff --git a/zerotrust/Trrafic-Identify/README.md b/zerotrust/Trrafic-Identify/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0e568b2f90aa9ab7121682d9b0f52ff466e135a0 --- /dev/null +++ b/zerotrust/Trrafic-Identify/README.md @@ -0,0 +1,25 @@ +### 项目介绍 + +- 本项目是一个基于Mindspore框架的异常流量识别模型。通过建立一个深度神经网络，来学习正常流量与异常流量的特征分布，然后进行网络流量行为分析，最终实现对异常流量攻击类型的识别与过滤。 + +### 相关技术 + +- 数据集选择CICIDS2017，这是加拿大网络安全研究所于2017年发布的数据集 +- 使用Pandas对CICIDS2017数据集进行数据预处理，筛选特征、清洗数据集 +- 使用scikit-learn机器学习库进行数据集的分割、数据标准化和特征编码 +- 使用Mindspore框架搭建DNN网络模型 + +### 模型设计流程 + +- 数据预处理 + - 整合原始数据 + - 筛选特征、特征编码 + - 数据集分割、标准化 +- 模型构建 + - 构造模型输入 + - 搭建DNN网络结构 + - 编译、训练模型 +- 模型优化 + - 模型推理验证 + - 调整激活函数、损失函数和优化器 + - 调节超参数 \ No newline at end of file diff --git a/zerotrust/Trrafic-Identify/code/DataProcessing.ipynb b/zerotrust/Trrafic-Identify/code/DataProcessing.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..af411868009132eca3a0285f0f4caf35c96d3393 --- /dev/null +++ b/zerotrust/Trrafic-Identify/code/DataProcessing.ipynb @@ -0,0 +1,818 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "\n", + "#设置行不限制数量\n", + "pd.set_option('display.max_rows',None)\n", + "#设置列不限制数量\n", + "pd.set_option('display.max_columns',None)\n", + "#设置value的显示长度为100，默认为50\n", + "pd.set_option('max_colwidth',100)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "\"\"\"原始数据合并\"\"\"\n", + "\n", + "data_path = \"./data/origin_data/\"\n", + "file_list = os.listdir(data_path) # 原始数据文件列表\n", + "data_list = [] # 存储每个文件处理后的dataframe数据\n", + "\n", + "# 依次处理每个数据文件\n", + "for file in file_list:\n", + " file_path = os.path.join(data_path, file)\n", + " df = pd.read_csv(file_path, index_col=None)\n", + " df.replace([np.inf, -np.inf, \"Infinity\", \"NaN\"], np.nan, inplace=True) # 将无穷值替换为缺失值\n", + " df.dropna(axis=\"rows\", how=\"any\", inplace=True) # 删除缺失值所在行\n", + " data_list.append(df)\n", + "\n", + "frame = pd.concat(data_list, axis=0, ignore_index=True) # 数据合并" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Destination PortFlow DurationTotal Fwd PacketsTotal Backward PacketsTotal Length of Fwd PacketsTotal Length of Bwd PacketsFwd Packet Length MaxFwd Packet Length MinFwd Packet Length MeanFwd Packet Length StdBwd Packet Length MaxBwd Packet Length MinBwd Packet Length MeanBwd Packet Length StdFlow Bytes/sFlow Packets/sFlow IAT MeanFlow IAT StdFlow IAT MaxFlow IAT MinFwd IAT TotalFwd IAT MeanFwd IAT StdFwd IAT MaxFwd IAT MinBwd IAT TotalBwd IAT MeanBwd IAT StdBwd IAT MaxBwd IAT MinFwd PSH FlagsBwd PSH FlagsFwd URG FlagsBwd URG FlagsFwd Header LengthBwd Header LengthFwd Packets/sBwd Packets/sMin Packet LengthMax Packet LengthPacket Length MeanPacket Length StdPacket Length VarianceFIN Flag CountSYN Flag CountRST Flag CountPSH Flag CountACK Flag CountURG Flag CountCWE Flag CountECE Flag CountDown/Up RatioAverage Packet SizeAvg Fwd Segment SizeAvg Bwd Segment SizeFwd Header Length.1Fwd Avg Bytes/BulkFwd Avg Packets/BulkFwd Avg Bulk RateBwd Avg Bytes/BulkBwd Avg Packets/BulkBwd Avg Bulk RateSubflow Fwd PacketsSubflow Fwd BytesSubflow Bwd PacketsSubflow Bwd BytesInit_Win_bytes_forwardInit_Win_bytes_backwardact_data_pkt_fwdmin_seg_size_forwardActive MeanActive StdActive MaxActive MinIdle MeanIdle StdIdle MaxIdle MinLabel
054865320120666.00.0000.00.04.000000e+06666666.666703.00.03333.00.03300.00.0000000400666666.6667000.000000666.00.00.00000100009.06.00.0400000002120033-11200.00.0000.00.000BENIGN
1550541091166666.00.0666.00.01.100917e+0518348.62385109.00.010910900.00.00000.00.000000020209174.3119279174.311927666.00.00.00000110019.06.06.0200000001616292560200.00.0000.00.000BENIGN
255055521166666.00.0666.00.02.307692e+0538461.5384652.00.0525200.00.00000.00.0000000202019230.76923019230.769230666.00.00.00000110019.06.06.0200000001616292560200.00.0000.00.000BENIGN
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" + ], + "text/plain": [ + " Destination Port Flow Duration Total Fwd Packets \\\n", + "0 54865 3 2 \n", + "1 55054 109 1 \n", + "2 55055 52 1 \n", + "\n", + " Total Backward Packets Total Length of Fwd Packets \\\n", + "0 0 12 \n", + "1 1 6 \n", + "2 1 6 \n", + "\n", + " Total Length of Bwd Packets Fwd Packet Length Max \\\n", + "0 0 6 \n", + "1 6 6 \n", + "2 6 6 \n", + "\n", + " Fwd Packet Length Min Fwd Packet Length Mean Fwd Packet Length Std \\\n", + "0 6 6.0 0.0 \n", + "1 6 6.0 0.0 \n", + "2 6 6.0 0.0 \n", + "\n", + " Bwd Packet Length Max Bwd Packet Length Min Bwd Packet Length Mean \\\n", + "0 0 0 0.0 \n", + "1 6 6 6.0 \n", + "2 6 6 6.0 \n", + "\n", + " Bwd Packet Length Std Flow Bytes/s Flow Packets/s Flow IAT Mean \\\n", + "0 0.0 4.000000e+06 666666.66670 3.0 \n", + "1 0.0 1.100917e+05 18348.62385 109.0 \n", + "2 0.0 2.307692e+05 38461.53846 52.0 \n", + "\n", + " Flow IAT Std Flow IAT Max Flow IAT Min Fwd IAT Total Fwd IAT Mean \\\n", + "0 0.0 3 3 3 3.0 \n", + "1 0.0 109 109 0 0.0 \n", + "2 0.0 52 52 0 0.0 \n", + "\n", + " Fwd IAT Std Fwd IAT Max Fwd IAT Min Bwd IAT Total Bwd IAT Mean \\\n", + "0 0.0 3 3 0 0.0 \n", + "1 0.0 0 0 0 0.0 \n", + "2 0.0 0 0 0 0.0 \n", + "\n", + " Bwd IAT Std Bwd IAT Max Bwd IAT Min Fwd PSH Flags Bwd PSH Flags \\\n", + "0 0.0 0 0 0 0 \n", + "1 0.0 0 0 0 0 \n", + "2 0.0 0 0 0 0 \n", + "\n", + " Fwd URG Flags Bwd URG Flags Fwd Header Length Bwd Header Length \\\n", + "0 0 0 40 0 \n", + "1 0 0 20 20 \n", + "2 0 0 20 20 \n", + "\n", + " Fwd Packets/s Bwd Packets/s Min Packet Length Max Packet Length \\\n", + "0 666666.666700 0.000000 6 6 \n", + "1 9174.311927 9174.311927 6 6 \n", + "2 19230.769230 19230.769230 6 6 \n", + "\n", + " Packet Length Mean Packet Length Std Packet Length Variance \\\n", + "0 6.0 0.0 0.0 \n", + "1 6.0 0.0 0.0 \n", + "2 6.0 0.0 0.0 \n", + "\n", + " FIN Flag Count SYN Flag Count RST Flag Count PSH Flag Count \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "\n", + " ACK Flag Count URG Flag Count CWE Flag Count ECE Flag Count \\\n", + "0 1 0 0 0 \n", + "1 1 1 0 0 \n", + "2 1 1 0 0 \n", + "\n", + " Down/Up Ratio Average Packet Size Avg Fwd Segment Size \\\n", + "0 0 9.0 6.0 \n", + "1 1 9.0 6.0 \n", + "2 1 9.0 6.0 \n", + "\n", + " Avg Bwd Segment Size Fwd Header Length.1 Fwd Avg Bytes/Bulk \\\n", + "0 0.0 40 0 \n", + "1 6.0 20 0 \n", + "2 6.0 20 0 \n", + "\n", + " Fwd Avg Packets/Bulk Fwd Avg Bulk Rate Bwd Avg Bytes/Bulk \\\n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "\n", + " Bwd Avg Packets/Bulk Bwd Avg Bulk Rate Subflow Fwd Packets \\\n", + "0 0 0 2 \n", + "1 0 0 1 \n", + "2 0 0 1 \n", + "\n", + " Subflow Fwd Bytes Subflow Bwd Packets Subflow Bwd Bytes \\\n", + "0 12 0 0 \n", + "1 6 1 6 \n", + "2 6 1 6 \n", + "\n", + " Init_Win_bytes_forward Init_Win_bytes_backward act_data_pkt_fwd \\\n", + "0 33 -1 1 \n", + "1 29 256 0 \n", + "2 29 256 0 \n", + "\n", + " min_seg_size_forward Active Mean Active Std Active Max Active Min \\\n", + "0 20 0.0 0.0 0 0 \n", + "1 20 0.0 0.0 0 0 \n", + "2 20 0.0 0.0 0 0 \n", + "\n", + " Idle Mean Idle Std Idle Max Idle Min Label \n", + "0 0.0 0.0 0 0 BENIGN \n", + "1 0.0 0.0 0 0 BENIGN \n", + "2 0.0 0.0 0 0 BENIGN " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "BENIGN 1741839\n", + "DoS Hulk 230124\n", + "PortScan 158804\n", + "DDoS 128025\n", + "DoS GoldenEye 10293\n", + "FTP-Patator 7935\n", + "SSH-Patator 5897\n", + "DoS slowloris 5796\n", + "DoS Slowhttptest 5499\n", + "Bot 1956\n", + "Web Attack � Brute Force 1507\n", + "Web Attack � XSS 652\n", + "Infiltration 36\n", + "Web Attack � Sql Injection 21\n", + "Heartbleed 11\n", + "Name: Label, dtype: int64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frame[\" Label\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "可以发现，数据集存在严重的的样本不均衡问题，正常流量（BENIGN）在数据集中的占比很大，而其他一些异常流量则占比很小，这就容易导致训练出的模型更偏向于将输入样本预测为占比大的那一类，实际应用场景中并不能达到我们预期的效果。为此我们对数据集进行平衡样本处理，仅选择前六个类别进行训练预测。" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "\"\"\"平衡数据集样本\"\"\"\n", + "BENIGN = frame[frame[' Label']=='BENIGN'][:250000]\n", + "DosHulk = frame[frame[' Label']=='DoS Hulk']\n", + "PortScan = frame[frame[' Label']=='PortScan']\n", + "DDoS = frame[frame[' Label']=='DDoS']\n", + "DoSGoldenEye = frame[frame[' Label']=='DoS GoldenEye']\n", + "FTPPatator = frame[frame[' Label']=='FTP-Patator']\n", + "\n", + "new_dataset = pd.concat([BENIGN, DosHulk, PortScan, DDos, DoSGoldenEye, FTPPatator])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# 选出11个典型特征进行训练\n", + "final_columns = [' Bwd Packet Length Min',' Subflow Fwd Bytes','Total Length of Fwd Packets',\n", + " ' Fwd Packet Length Mean',' Bwd Packet Length Std',' Flow Duration',' Flow IAT Std',\n", + " 'Init_Win_bytes_forward',' Bwd Packets/s', ' PSH Flag Count',' Average Packet Size',' Label' ]\n", + "dataset = new_dataset[final_columns]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.7/site-packages/ipykernel/__main__.py:8: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\"\"\"特征编码\"\"\"\n", + "# 将类别特征转换为离散性数值\n", + "y = dataset[' Label'] # 经过对数据的进一步观察，发现只有Label列为类别特征\n", + "le = LabelEncoder() # 编码器实例化\n", + "le = le.fit(y) # 导入数据\n", + "label = le.transform(y) # 接口调取结果\n", + "dataset[' Label'] = label # 更新对应属性列的值" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 250000\n", + "2 230124\n", + "4 158804\n", + "1 10293\n", + "3 7935\n", + "Name: Label, dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 查看编码后的Label属性列\n", + "dataset[\" Label\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['BENIGN', 'DoS GoldenEye', 'DoS Hulk', 'FTP-Patator', 'PortScan'],\n", + " dtype=object)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 编码前后的对应关系\n", + "match = le.inverse_transform([0, 1, 2, 3, 4])\n", + "match" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# 保存清洗过的数据为csv文件\n", + "dataset.to_csv('./data/processed_data/dataset6classes.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:MindSpore] *", + "language": "python", + "name": "conda-env-MindSpore-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/zerotrust/Trrafic-Identify/code/TrainDNN.ipynb b/zerotrust/Trrafic-Identify/code/TrainDNN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9f28ac98168e4367451ae1bc769443551ffb7511 --- /dev/null +++ b/zerotrust/Trrafic-Identify/code/TrainDNN.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "541b1de9-2f3b-4461-a72e-e13d63a06746", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import mindspore as ms\n", + "import mindspore.nn as nn\n", + "import mindspore.dataset as ds\n", + "import mindspore.dataset.transforms.c_transforms as C\n", + "from mindspore import dtype as mstype\n", + "from mindspore.common.initializer import Normal\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "import joblib" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "21c0f7c5-06f1-41d7-96f9-cfbfea132a88", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# 读取数据集文件\n", + "dataset = pd.read_csv('./data/processed_data/dataset6classes.csv')\n", + "dataset.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2a5d023e-f5ca-46b8-a101-4236a66078d1", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 2, 4, 1, 3])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = dataset.drop('Unnamed: 0', 1)\n", + "dataset[\" Label\"].unique() # 查看Label的类别" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e36da384-da3b-42c6-b5f8-af20ecca936b", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "feature_lenth = dataset.shape[1]\n", + "data = dataset.iloc[:,:feature_lenth-1] # 获取特征属性列\n", + "label = dataset[' Label'] # 获取Label属性列，\n", + "\n", + "# 分割数据集为训练集、验证集和测试集\n", + "X_train, X_test, y_train, y_test = train_test_split(data, label,test_size=0.1, random_state=1)\n", + "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train,test_size=0.2, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6b84277a-77df-4a34-929b-5c06585b8e6f", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['./StandardScaler2.save']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 将训练集的均值和方差作为总样本的参数分布\n", + "standard_scaler = StandardScaler()\n", + "standard_scaler.fit(X_train)\n", + "\n", + "# 对数据集进行标准化处理\n", + "X_train = standard_scaler.transform(X_train)\n", + "X_val = standard_scaler.transform(X_val)\n", + "X_test = standard_scaler.transform(X_test)\n", + "\n", + "# 保存缩放器模型\n", + "scaler_save_path = \"./model_saved/StandardScaler.save\"\n", + "joblib.dump(standard_scaler, scaler_save_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "37aba3a6-fa72-42fd-86b7-9efb15be094f", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train examples: 473152\n", + "validation examples 118288\n", + "test examples: 65716\n" + ] + } + ], + "source": [ + "print('train examples:', len(X_train))\n", + "print( 'validation examples', len(X_val))\n", + "print('test examples:', len(X_test) )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "caddd578-6ae4-435f-813c-4d612ef632e4", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "\n", + "# 自定义数据源\n", + "class MyAccessible:\n", + " def __init__(self, X, y):\n", + " self._data = np.array(X)\n", + " self._label = np.array(y)\n", + "\n", + " def __getitem__(self, index):\n", + " return self._data[index], self._label[index]\n", + "\n", + " def __len__(self):\n", + " return len(self._data)\n", + "\n", + "# 创建数据集，使之符合模型输入的要求\n", + "def Ceate_dataset(X, y, shuffle_size = 200, batch_size=128):\n", + " ds.config.set_seed(123) # 设置随机种子，保证每次随机化处理的结果相同\n", + " dataset = ds.GeneratorDataset(source=MyAccessible(X, y), column_names=[\"data\", \"label\"])\n", + " dataset = dataset.map(operations=C.TypeCast(mstype.float32), input_columns=[\"data\"]) # 特征属性列的数据类型统一为float32\n", + " dataset = dataset.map(operations=C.TypeCast(mstype.int32), input_columns=[\"label\"]) # 类别属性的数据类型设为int32\n", + " dataset = dataset.shuffle(shuffle_size)\n", + " dataset = dataset.batch(batch_size)\n", + " return dataset\n", + "\n", + "# 得到训练集、验证集和测试集\n", + "data_train = Ceate_dataset(X_train, y_train)\n", + "data_val = Ceate_dataset(X_val, y_val)\n", + "data_test = Ceate_dataset(X_test, y_test)\n", + "\n", + "# # 查看数据集中的前五条数据，观察数据类型和大小\n", + "# iterator = data_test.create_dict_iterator()\n", + "# i =0\n", + "# for item in iterator:\n", + "# # item is a dict\n", + "# i +=1\n", + "# print(item)\n", + "# if i==10:\n", + "# break" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1e957d86-83d7-49af-ab35-c32b79c28e0a", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "\n", + "class DNN(nn.Cell):\n", + " \"\"\"定义深度神经网络结构\"\"\"\n", + " def __init__(self, input_dims=11, output_dims=5, dropout_ratio=0.2):\n", + " super(DNN, self).__init__()\n", + " self.input_dims = input_dims\n", + " self.output_dims = output_dims\n", + " self.dropout_ratio = dropout_ratio\n", + " # 定义所需要的运算\n", + " self.fc1 = nn.Dense(self.input_dims, 512, weight_init=Normal(0.02))\n", + " self.fc2 = nn.Dense(512, 256, weight_init=Normal(0.02))\n", + " self.fc3 = nn.Dense(256, 128, weight_init=Normal(0.02))\n", + " self.fc4 = nn.Dense(128, 64, weight_init=Normal(0.02))\n", + " self.fc5 = nn.Dense(64, self.output_dims, weight_init=Normal(0.02))\n", + " self.relu = nn.ReLU()\n", + " self.log_softmax = nn.LogSoftmax()\n", + " self.dropout = nn.Dropout(keep_prob=dropout_ratio)\n", + "\n", + " # 使用定义好的运算构建前向网络\n", + " def construct(self, x):\n", + "\n", + " x = self.fc1(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + "\n", + " x = self.fc2(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + "\n", + " x = self.fc3(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + "\n", + " x = self.fc4(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + "\n", + " x = self.fc5(x)\n", + " x = self.log_softmax(x)\n", + " return x\n", + "# 网络实例化\n", + "net = DNN()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6b10ed8c-5b3a-4dca-8cf1-8905e55a359e", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from mindspore import Model\n", + "from mindspore.nn import SoftmaxCrossEntropyWithLogits\n", + "from mindspore.nn import SGD, Adam\n", + "from mindspore.nn.metrics import Accuracy\n", + "\n", + "lr = 0.0001 # 学习率\n", + "#定义损失函数\n", + "loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n", + "#定义优化器（待更新的参数，学习率，权重衰减）\n", + "optim = Adam(params=net.trainable_params(), learning_rate=lr, weight_decay=0.01)\n", + "#实例化模型对象（网络结构，损失函数，优化器，评价指标）\n", + "model = Model(network=net,loss_fn=loss,optimizer=optim,metrics={'accuracy': Accuracy()})" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "617bff1f-36f6-4860-b9b4-7b288adb5e0e", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------开始训练-------------\n", + "epoch: 1 step: 1000, loss is 0.9638172388076782\n", + "epoch: 1 step: 2000, loss is 0.8211514353752136\n", + "epoch: 1 step: 3000, loss is 0.5580129623413086\n", + "epoch: 2 step: 303, loss is 0.37132251262664795\n", + "epoch: 2 step: 1303, loss is 0.3668070137500763\n", + "epoch: 2 step: 2303, loss is 0.3831019103527069\n", + "epoch: 2 step: 3303, loss is 0.34128567576408386\n", + "验证集准确率为: {'accuracy': 0.9162298796158528}\n", + "epoch: 3 step: 606, loss is 0.404092937707901\n", + "epoch: 3 step: 1606, loss is 0.5582598447799683\n", + "epoch: 3 step: 2606, loss is 0.35104385018348694\n", + "epoch: 3 step: 3606, loss is 0.38596564531326294\n", + "epoch: 4 step: 909, loss is 0.294807493686676\n", + "epoch: 4 step: 1909, loss is 0.4008302390575409\n", + "epoch: 4 step: 2909, loss is 0.27310097217559814\n", + "验证集准确率为: {'accuracy': 0.9305762207493575}\n", + "epoch: 5 step: 212, loss is 0.3179163634777069\n", + "epoch: 5 step: 1212, loss is 0.2543521523475647\n", + "epoch: 5 step: 2212, loss is 0.29405146837234497\n", + "epoch: 5 step: 3212, loss is 0.26114270091056824\n", + "epoch: 6 step: 515, loss is 0.249850794672966\n", + "epoch: 6 step: 1515, loss is 0.1967962235212326\n", + "epoch: 6 step: 2515, loss is 0.3437327742576599\n", + "epoch: 6 step: 3515, loss is 0.21695563197135925\n", + "验证集准确率为: {'accuracy': 0.931480792641688}\n", + "epoch: 7 step: 818, loss is 0.2360164076089859\n", + "epoch: 7 step: 1818, loss is 0.26525306701660156\n", + "epoch: 7 step: 2818, loss is 0.47287988662719727\n", + "epoch: 8 step: 121, loss is 0.3011430501937866\n", + "epoch: 8 step: 1121, loss is 0.2848264276981354\n", + "epoch: 8 step: 2121, loss is 0.21570047736167908\n", + "epoch: 8 step: 3121, loss is 0.25052201747894287\n", + "验证集准确率为: {'accuracy': 0.9301873393750846}\n", + "epoch: 9 step: 424, loss is 0.3193442225456238\n", + "epoch: 9 step: 1424, loss is 0.2135888934135437\n", + "epoch: 9 step: 2424, loss is 0.24232132732868195\n", + "epoch: 9 step: 3424, loss is 0.3493567109107971\n", + "epoch: 10 step: 727, loss is 0.2526402175426483\n", + "epoch: 10 step: 1727, loss is 0.3711300790309906\n", + "epoch: 10 step: 2727, loss is 0.24978187680244446\n", + "验证集准确率为: {'accuracy': 0.9301788854321655}\n", + "epoch: 11 step: 30, loss is 0.3838188350200653\n", + "epoch: 11 step: 1030, loss is 0.3312799036502838\n", + "epoch: 11 step: 2030, loss is 0.35113874077796936\n", + "epoch: 11 step: 3030, loss is 0.28902480006217957\n", + "epoch: 12 step: 333, loss is 0.3289121687412262\n", + "epoch: 12 step: 1333, loss is 0.27037400007247925\n", + "epoch: 12 step: 2333, loss is 0.36806437373161316\n", + "epoch: 12 step: 3333, loss is 0.24908971786499023\n", + "验证集准确率为: {'accuracy': 0.930787569322332}\n", + "epoch: 13 step: 636, loss is 0.31657737493515015\n", + "epoch: 13 step: 1636, loss is 0.3002266585826874\n", + "epoch: 13 step: 2636, loss is 0.29410573840141296\n", + "epoch: 13 step: 3636, loss is 0.2902418076992035\n", + "epoch: 14 step: 939, loss is 0.34310269355773926\n", + "epoch: 14 step: 1939, loss is 0.2950718104839325\n", + "epoch: 14 step: 2939, loss is 0.21274831891059875\n", + "验证集准确率为: {'accuracy': 0.931159542810767}\n", + "epoch: 15 step: 242, loss is 0.3990159332752228\n", + "epoch: 15 step: 1242, loss is 0.2789316475391388\n", + "epoch: 15 step: 2242, loss is 0.23281070590019226\n", + "epoch: 15 step: 3242, loss is 0.3102906048297882\n", + "epoch: 16 step: 545, loss is 0.2600497901439667\n", + "epoch: 16 step: 1545, loss is 0.2706286311149597\n", + "epoch: 16 step: 2545, loss is 0.37438470125198364\n", + "epoch: 16 step: 3545, loss is 0.30041372776031494\n", + "验证集准确率为: {'accuracy': 0.9296040173136751}\n", + "epoch: 17 step: 848, loss is 0.26352012157440186\n", + "epoch: 17 step: 1848, loss is 0.2437521368265152\n", + "epoch: 17 step: 2848, loss is 0.2724604904651642\n", + "epoch: 18 step: 151, loss is 0.2848968207836151\n", + "epoch: 18 step: 1151, loss is 0.3152933120727539\n", + "epoch: 18 step: 2151, loss is 0.2985284626483917\n", + "epoch: 18 step: 3151, loss is 0.2768436670303345\n", + "验证集准确率为: {'accuracy': 0.9309989178953064}\n", + "epoch: 19 step: 454, loss is 0.31289243698120117\n", + "epoch: 19 step: 1454, loss is 0.2889769375324249\n", + "epoch: 19 step: 2454, loss is 0.36934804916381836\n", + "epoch: 19 step: 3454, loss is 0.27359330654144287\n", + "epoch: 20 step: 757, loss is 0.3022765517234802\n", + "epoch: 20 step: 1757, loss is 0.3043055534362793\n", + "epoch: 20 step: 2757, loss is 0.35668396949768066\n", + "验证集准确率为: {'accuracy': 0.9310580954957393}\n", + "---------------训练结束-------------\n" + ] + } + ], + "source": [ + "from mindspore.train.callback import LossMonitor, TimeMonitor, Callback,CheckpointConfig,ModelCheckpoint\n", + "\n", + "# 自定义回调类（继承Callback类），用于计算输出验证集准确率\n", + "class ValCallBack(Callback):\n", + " def __init__(self, model, val_dataset, epochs_to_val, per_val, dataset_sink_mode=False):\n", + " self.model = model\n", + " self.val_dataset = val_dataset # 输出验证集准确率的epoch间隔数\n", + " self.epochs_to_val = epochs_to_val # 一个记录当前epoch和val_acc值的字典\n", + " self.per_val = per_val\n", + " self.dataset_sink_mode = dataset_sink_mode\n", + "\n", + " def epoch_end(self, run_context):\n", + " # 获取到现在的epoch数\n", + " cb_param = run_context.original_args()\n", + " cur_epoch = cb_param.cur_epoch_num\n", + " # 如果达到进行验证的epoch数，则进行以下验证操作\n", + " if cur_epoch % self.epochs_to_val == 0:\n", + " # 此处model设定的metrics是准确率Accuracy\n", + " acc = self.model.eval(self.val_dataset, dataset_sink_mode=self.dataset_sink_mode)\n", + " self.per_val[\"epoch\"].append(cur_epoch)\n", + " self.per_val[\"acc\"].append(acc)\n", + " print(\"验证集准确率为: {}\".format(acc))\n", + "\n", + "#实例化回调对象\n", + "epochs_to_val = 2 # 每隔2个epoch在验证集上进行一次验证\n", + "val_acc = {'epoch':[], 'acc':[]} # 记录当前epoch数和acc值\n", + "valAcc_cb = ValCallBack(model,data_val,epochs_to_val, val_acc)\n", + "# 回调训练过程中的loss值\n", + "loss_cb = LossMonitor(per_print_times=1000)\n", + "\n", + "epoches = 20\n", + "print('---------------开始训练-------------')\n", + "model.train(epoches, data_train, callbacks=[loss_cb,valAcc_cb], dataset_sink_mode=False)\n", + "print('---------------训练结束-------------')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cc8aa5db-5db9-4647-8efb-c3545ac25fcb", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The accuracy of the test dataset is {'accuracy': 0.9307058608949417}\n" + ] + } + ], + "source": [ + "# 使用验证集评估模型，打印总体准确率\n", + "acc = model.eval(data_test)\n", + "print('The accuracy of the test dataset is {}'.format(acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f68cb274-dde3-49c2-9dda-cafb4d010e7f", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "ms.save_checkpoint(net, \"DNN.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a29dd92-1893-4fc5-8242-f835fdeb6cbb", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + 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"print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/zerotrust/Trrafic-Identify/code/apply.py b/zerotrust/Trrafic-Identify/code/apply.py new file mode 100644 index 0000000000000000000000000000000000000000..a9b6cfe805042618c554c4dded668447138787f3 --- /dev/null +++ b/zerotrust/Trrafic-Identify/code/apply.py @@ -0,0 +1,81 @@ +import numpy as np +import pandas as pd +import joblib +import mindspore as ms +from net import DNN +from mindspore import Model, Tensor +from mindspore import load_checkpoint, load_param_into_net + +standard_scaler = joblib.load('./model_saved/StandardScaler.save') # 加载数据缩放器模型 + +param_dict = load_checkpoint("./model_saved/DNN.ckpt") # 加载模型参数 +net = DNN() # 实例化网络模型 +load_param_into_net(net, param_dict) # 将参数加载到网络中 +model = Model(net) # 获取模型结构及参数 + +# 将模型预测编码标签与原始类别标签进行映射 +encode_label = [0, 1, 2, 3, 4] +origin_label = ['BENIGN', 'DoS GoldenEye', 'DoS Hulk', 'FTP-Patator', 'PortScan'] +class_map = dict(zip(encode_label, origin_label)) + + +def identify(input_data): + """ + 流量的分类与识别 + :param input_data: 原始输入数据，是一个含有11个数据的numpy数组，其中每一行的数据分别代表以下11个特征： + Bwd Packet Length Min :数据包在反向上的最小值 + Subflow Fwd Bytes :子流在正向中的平均字节数 + Total Length of Fwd Packets :正向数据包的总大小 + Fwd Packet Length Mean :数据包在正向上的平均大小 + Bwd Packet Length Std :数据包反向标准偏差大小 + Flow Duration :数据流持续时间 + Flow IAT Std :两个流之间的标准差 + Init_Win_bytes_forward :在正向的初始窗口中发送的字节数 + Bwd Packets/s :每秒中后向包的数量 + PSH Flag Count :带有 PSH 的包数量 + Average Packet Size :数据包的平均大小 + :return: 模型预测的流量类型 + """ + + # 数组重塑 + data = input_data.reshape(1, 11) + # 数据标准化处理 + data = standard_scaler.transform(data) + # 转为mindspore张量形式作为模型输入 + data = Tensor(data, ms.float32) + predictions = model.predict(data) + label = class_map[int(np.argmax(predictions))] + + # 流量的分类与识别 + if label == 'BENIGN': # 正常流量 + print("Normal Traffic!") + else : # 异常流量 + print("Abnormal Traffic! Attack type:",label) + return label + + +if __name__ == '__main__': + + """模型使用的测试样例""" + import warnings + warnings.filterwarnings("ignore") + + # 流量鉴别模型测试 + dataset = pd.read_csv('./data/processed_data/dataset6classes.csv') + dataset = dataset.drop('Unnamed: 0', 1) + + # 从数据集中随机抽选20条测试数据 + np.random.seed(0) + test_data = dataset.loc[np.random.randint(low=0, high=len(dataset), size=20)] + test_data = np.array(test_data) + data = test_data[:, :11] # 获取测试数据的特征数据 + real_lables = test_data[:,-1] # 获取测试数据的真实标签 + for i in range(len(data)): + data_flow = data[i,:] + real_lable = class_map[int(real_lables[i])] + np.set_printoptions(formatter={'float': '{: 0.3f}'.format}) + print( "Input_data:(Real Label is {})\n".format(real_lable), data_flow) + # 对输入的每条数据流进行流量鉴别 + print("Output:") + pred_label = identify(data_flow) + print("\n") \ No newline at end of file diff --git a/zerotrust/Trrafic-Identify/code/net.py b/zerotrust/Trrafic-Identify/code/net.py new file mode 100644 index 0000000000000000000000000000000000000000..28ceb4ef85fbc33aa97cd17f30b3f96a0c22c74e --- /dev/null +++ b/zerotrust/Trrafic-Identify/code/net.py @@ -0,0 +1,43 @@ +from mindspore import nn +from mindspore.common.initializer import Normal + + +class DNN(nn.Cell): + """定义深度神经网络相关算子""" + def __init__(self, input_dims=11, output_dims=6, dropout_ratio=0.2): + super(DNN, self).__init__() + self.input_dims = input_dims + self.output_dims = output_dims + self.dropout_ratio = dropout_ratio + # 定义所需要的运算 + self.fc1 = nn.Dense(self.input_dims, 512, weight_init=Normal(0.02)) + self.fc2 = nn.Dense(512, 256, weight_init=Normal(0.02)) + self.fc3 = nn.Dense(256, 128, weight_init=Normal(0.02)) + self.fc4 = nn.Dense(128, 64, weight_init=Normal(0.02)) + self.fc5 = nn.Dense(64, self.output_dims, weight_init=Normal(0.02)) + self.relu = nn.ReLU() + self.log_softmax = nn.LogSoftmax() + self.dropout = nn.Dropout(keep_prob=dropout_ratio) + + # 使用定义好的算子构建前向网络 + def construct(self, x): + + x = self.fc1(x) + x = self.relu(x) + x = self.dropout(x) + + x = self.fc2(x) + x = self.relu(x) + x = self.dropout(x) + + x = self.fc3(x) + x = self.relu(x) + x = self.dropout(x) + + x = self.fc4(x) + x = self.relu(x) + x = self.dropout(x) + + x = self.fc5(x) + x = self.log_softmax(x) + return x \ No newline at end of file diff --git a/zerotrust/Trrafic-Identify/data/processed_data/dataset6classes.csv b/zerotrust/Trrafic-Identify/data/processed_data/dataset6classes.csv new file mode 100644 index 0000000000000000000000000000000000000000..3ea206ccb4ab517519e9afe1b59e93ac04258f40 --- /dev/null +++ b/zerotrust/Trrafic-Identify/data/processed_data/dataset6classes.csv @@ -0,0 +1,657157 @@ +, Bwd Packet Length Min, Subflow Fwd Bytes,Total Length of Fwd Packets, Fwd Packet Length Mean, Bwd Packet Length Std, Flow Duration, Flow IAT Std,Init_Win_bytes_forward, Bwd Packets/s, PSH Flag Count, Average Packet Size, Label +0,0,12,12,6.0,0.0,3,0.0,33,0.0,0,9.0,0 +1,6,6,6,6.0,0.0,109,0.0,29,9174.311926999999,0,9.0,0 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