resnet50-train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import pickle\n",
    "from sklearn.utils import resample\n",
    "from collections import Counter\n",
    "import math\n",
    "from sklearn.model_selection import train_test_split\n",
    "import helper\n",
    "import dataset\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from resnet1d.net1d import Net1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2830743, 79)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_files = [name for name in os.listdir('./data')]\n",
    "\n",
    "dfs = []\n",
    "\n",
    "for i in range(len(data_files)):\n",
    "    fname = './data/' + data_files[i]\n",
    "    df = pd.read_csv(fname)\n",
    "    # df.columns = [x.strip().lstrip() for x in df.columns]\n",
    "\n",
    "    dfs.append(df)\n",
    "\n",
    "df = pd.concat(dfs, axis=0, ignore_index=True)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2830743, 79)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove spaces in the front and the end of the column names for better human reading\n",
    "df.columns = [x.lstrip().strip().replace('�', '-') for x in df.columns]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2827876, 79)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace inf values to nan\n",
    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "df.dropna(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_val = train_test_split(df, stratify=df['Label'], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'BENIGN': 1817055,\n",
       "         'DoS Hulk': 184099,\n",
       "         'PortScan': 127043,\n",
       "         'DDoS': 102420,\n",
       "         'DoS GoldenEye': 8234,\n",
       "         'FTP-Patator': 6348,\n",
       "         'SSH-Patator': 4717,\n",
       "         'DoS slowloris': 4637,\n",
       "         'DoS Slowhttptest': 4399,\n",
       "         'Bot': 1565,\n",
       "         'Web Attack � Brute Force': 1206,\n",
       "         'Web Attack � XSS': 522,\n",
       "         'Infiltration': 29,\n",
       "         'Web Attack � Sql Injection': 17,\n",
       "         'Heartbleed': 9})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(df_train['Label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "445243\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({'BENIGN': 206780,\n",
       "         'DoS Hulk': 92049,\n",
       "         'PortScan': 63521,\n",
       "         'DDoS': 51210,\n",
       "         'DoS GoldenEye': 8234,\n",
       "         'FTP-Patator': 6348,\n",
       "         'SSH-Patator': 4717,\n",
       "         'DoS slowloris': 4637,\n",
       "         'DoS Slowhttptest': 4399,\n",
       "         'Bot': 1565,\n",
       "         'Web Attack � Brute Force': 1206,\n",
       "         'Web Attack � XSS': 522,\n",
       "         'Infiltration': 29,\n",
       "         'Web Attack � Sql Injection': 17,\n",
       "         'Heartbleed': 9})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "sampling_strategy = dict(Counter(df_train['Label']))\n",
    "attack_cnt = 0\n",
    "\n",
    "for k, v in sampling_strategy.items():\n",
    "    if v < 20000 or k == 'BENIGN': continue\n",
    "\n",
    "    # when samples count bigger than 10k, truncate in half\n",
    "    sampling_strategy[k] = v // 2\n",
    "    attack_cnt += v // 2\n",
    "\n",
    "sampling_strategy['BENIGN'] = attack_cnt\n",
    "\n",
    "under_sampler = RandomUnderSampler(sampling_strategy=sampling_strategy, random_state=42)\n",
    "\n",
    "X_train = df_train.drop(['Label'], axis=1)\n",
    "y_train = df_train['Label']\n",
    "\n",
    "X_train, y_train = under_sampler.fit_resample(X_train, y_train)\n",
    "\n",
    "print(y_train.shape[0])\n",
    "Counter(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "pca__n_components = 20\n",
    "\n",
    "preprocessor = Pipeline(steps=[\n",
    "    (\"imputer\", SimpleImputer(strategy='most_frequent')),\n",
    "    (\"normalize\", MinMaxScaler()),\n",
    "    (\"PCA\", PCA(n_components=pca__n_components))\n",
    "])\n",
    "\n",
    "label_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train = preprocessor.fit_transform(X_train)\n",
    "\n",
    "# doing this since we need all labels in case there's any labels not found in test set\n",
    "label_encoder.fit(df['Label'])\n",
    "\n",
    "y_train = label_encoder.transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./dist/preprocessor-resnet.pkl', 'wb') as f:\n",
    "    pickle.dump(preprocessor, f)\n",
    "\n",
    "with open('./dist/labelencoder-resnet.pkl', 'wb') as f:\n",
    "    pickle.dump(label_encoder, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gen argument views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(445243, 3, 20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_smote, _ = helper.smote_sampling(X_train, y_train)\n",
    "X_gaussion = helper.add_gaussion(X_smote)\n",
    "X_flip = helper.invert_array(X_train)\n",
    "\n",
    "X_new = np.dstack([X_smote, X_gaussion, X_flip])\n",
    "X_new = X_new.transpose((0, 2, 1))\n",
    "\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(445243, 1, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = np.expand_dims(X_train, axis=2).transpose((0, 2, 1))\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "org_dataset = dataset.NumpyDataset(X_train, y_train)\n",
    "pos_dataset = dataset.NumpyDataset(X_new, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best params found by hyper parameters tunning\n",
    "best_params = {'embedding_size': 34, 'lr': 0.010848038400629992, 'weight_decay': 0.00012209180832556052}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train model and save checkpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "import time\n",
    "import warnings\n",
    "import optuna\n",
    "from torch.utils.data import DataLoader\n",
    "from pytorch_metric_learning.losses import SelfSupervisedLoss, NTXentLoss\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import cross_validate, StratifiedKFold\n",
    "from models import Extractor, Projector, ExtractorMLP\n",
    "from models import ExtractorMLP\n",
    "from itertools import cycle\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "start_time = time.perf_counter()\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "embedding_size = best_params['embedding_size']\n",
    "\n",
    "extractor_1d = Extractor(n_features=pca__n_components, n_channels=1, embedding_size=embedding_size).to(device)\n",
    "extractor_3d = Extractor(n_features=pca__n_components, n_channels=3, embedding_size=embedding_size).to(device)\n",
    "\n",
    "projector = Projector(embedding_size=embedding_size).to(device)\n",
    "\n",
    "lr = best_params['lr']\n",
    "weight_decay = best_params['weight_decay']\n",
    "batch_size = 1024\n",
    "epochs = 200\n",
    "\n",
    "org_dataloader = DataLoader(org_dataset, batch_size=batch_size,)\n",
    "pos_dataloader = DataLoader(pos_dataset, batch_size=batch_size)\n",
    "\n",
    "optimizer_1d = torch.optim.Adam(extractor_1d.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "optimizer_3d = torch.optim.Adam(extractor_3d.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "optimizer_projector = torch.optim.Adam(projector.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "\n",
    "loss_fn = NTXentLoss()\n",
    "loss_fn = SelfSupervisedLoss(loss_fn, symmetric=False).to(device)\n",
    "\n",
    "writer = SummaryWriter(log_dir='./runs')\n",
    "\n",
    "cnt = 0\n",
    "num_epoch2save = 5\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    if epoch != 0 and epoch % num_epoch2save == 0:\n",
    "        helper.store_checkpoint(model=extractor_1d, name='extractor1d', epoch=epoch, prefix='resnet')\n",
    "        helper.store_checkpoint(model=extractor_3d, name='extractor3d', epoch=epoch, prefix='resnet')\n",
    "        helper.store_checkpoint(model=projector, name='projector', epoch=epoch, prefix='resnet')\n",
    "\n",
    "    total_loss = 0\n",
    "    for item1, item2 in zip(org_dataloader, cycle(pos_dataloader)):\n",
    "        X_batch, _ = item1\n",
    "        X_new_batch, _ = item2\n",
    "\n",
    "        X_batch = X_batch.to(device)\n",
    "        X_new_batch = X_new_batch.to(device)\n",
    "\n",
    "        optimizer_1d.zero_grad()\n",
    "        optimizer_3d.zero_grad()\n",
    "        optimizer_projector.zero_grad()\n",
    "\n",
    "        embedding_1d = extractor_1d(X_batch)\n",
    "        embedding_3d = extractor_3d(X_new_batch)\n",
    "        projected_1d = projector(embedding_1d)\n",
    "        projected_3d = projector(embedding_3d)\n",
    "\n",
    "        loss = loss_fn(projected_1d, projected_3d)\n",
    "        loss.backward()\n",
    "\n",
    "        total_loss += loss.item()\n",
    "    \n",
    "        optimizer_1d.step()\n",
    "        optimizer_3d.step()\n",
    "        optimizer_projector.step()\n",
    "\n",
    "    cnt += 1\n",
    "\n",
    "    print(f'[{epoch+1}/{epochs}]: {total_loss}')\n",
    "    \n",
    "    writer.add_scalar('Loss/train-Resnet50-prod', total_loss, cnt)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./dist/resnet/extractor1d-final.pkl', 'wb') as f:\n",
    "    pickle.dump(extractor_1d, f)"
   ]
  }
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