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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Install Necessary Packages"
],
"metadata": {
"id": "GUB8N3k9fq-E"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zt59bSq5vcnA"
},
"outputs": [],
"source": [
"#Necessary installations\n",
"!pip install datasets evaluate transformers[sentencepiece]\n",
"!pip install huggingface_hub\n",
"!pip install pandas\n",
"!pip install imblearn\n",
"!pip install torch"
]
},
{
"cell_type": "markdown",
"source": [
"# Load the Dataset"
],
"metadata": {
"id": "9lyEyWBic5RN"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QJDszQKe6oxK"
},
"outputs": [],
"source": [
"from datasets import Features, Value, ClassLabel\n",
"import pandas as pd\n",
"\n",
"from datasets import load_dataset\n",
"dataset = load_dataset(\"19kmunz/iot-23-preprocessed-minimumcolumns\")\n",
"print(dataset.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wRjakUpXD3D9"
},
"source": [
"# Oversample the Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wzU5AHGxD2Ut"
},
"outputs": [],
"source": [
"from imblearn.over_sampling import SMOTE\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mT027c7R1t7n"
},
"outputs": [],
"source": [
"df = dataset['train'].to_pandas()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "v2l9xGpr6bZc"
},
"outputs": [],
"source": [
"# Separate features and target\n",
"features = ['id.resp_p', 'proto', 'conn_state', 'orig_pkts', 'orig_ip_bytes', 'resp_ip_bytes']\n",
"X = df[features]\n",
"y = df['label']"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SlFbgG_69B1K"
},
"source": [
"ADASYN and SMOTE oversampling algorithm expects numeric data, but features like proto is non-numeric categorical column. SMOTE cannot handle the string values like 'tcp' in those columns. So, I applied one hot encoding to categorical columns and then applied SMOTE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8zSNEGIiWjMZ"
},
"outputs": [],
"source": [
"#########################################NEWWWW#############################################\n",
"# Define categorical columns to be label-encoded\n",
"cat_cols = ['proto', 'conn_state']\n",
"\n",
"# Initialize a dictionary to store label encoders for each column\n",
"label_encoders = {}\n",
"label_encoded_columns = {} # Store label-encoded columns\n",
"\n",
"for col in cat_cols:\n",
" le = LabelEncoder()\n",
" label_encoded = le.fit_transform(df[col])\n",
" df[col + '_label'] = label_encoded # Create new columns with label-encoded data\n",
" label_encoders[col] = le\n",
" label_encoded_columns[col] = label_encoded\n",
"# Get numeric columns\n",
"num_cols = ['id.resp_p','orig_pkts', 'orig_ip_bytes', 'resp_ip_bytes']\n",
"\n",
"# Extract numeric columns\n",
"X_num = df[num_cols]\n",
"\n",
"# Concatenate label-encoded columns and numeric columns\n",
"X_combined = pd.concat([df[['proto_label', 'conn_state_label']], X_num], axis=1)\n",
"\n",
"# Store the labels in y_os\n",
"y_os = df['label']\n",
"y_os1 = df['label'].apply(lambda x: 0 if x == \"Benign\" else 1)\n",
"\n",
"# Specify desired number of samples\n",
"#k_neighbors = 10000 - y_os.shape[0]\n",
"\n",
"# Perform oversampling using SMOTE\n",
"smote = SMOTE(sampling_strategy={0: 5000, 1: 5000})\n",
"X_combined_os, Y_combined_os = smote.fit_resample(X_combined, y_os1)"
]
},
{
"cell_type": "code",
"source": [
"# Print new class counts\n",
"print(Y_combined_os.value_counts())\n",
"print(X_combined_os.shape)"
],
"metadata": {
"id": "mZ1iMnEIkVAj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Split the Dataset"
],
"metadata": {
"id": "oO9g2nhlbr3o"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OzJI6451n4tE"
},
"outputs": [],
"source": [
"# Manually define the column names\n",
"column_names = ['proto_label', 'conn_state_label', 'id.resp_p','orig_pkts', 'orig_ip_bytes', 'resp_ip_bytes']\n",
"result_column = ['label']\n",
"\n",
"# Create a new DataFrame with the oversampled data and specified column names\n",
"X_combined_os_df = pd.DataFrame(X_combined_os, columns=column_names)\n",
"Y_combined_os_df = pd.DataFrame(Y_combined_os, columns=result_column)\n",
"\n",
"# Print the first 5 rows of the oversampled data\n",
"print(X_combined_os_df.shape)\n",
"print(X_combined_os_df.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YwnVJ7RqKFRD"
},
"outputs": [],
"source": [
"# Split oversampled data\n",
"\n",
"# Initial split into train and temp test sets\n",
"X_train, X_temp, y_train, y_temp = train_test_split(X_combined_os_df, Y_combined_os_df, test_size=0.2, random_state=42)\n",
"\n",
"# Split oversampled data\n",
"X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.2, random_state=42)\n",
"\n",
"print(\"Oversampled dataset shape:\", X_combined_os.shape)\n",
"print(\"X_train shape:\", X_train.shape)\n",
"print(\"X_test shape:\", X_test.shape)\n",
"print(\"X_val shape:\", X_val.shape)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WHobtry9LI_d"
},
"source": [
"# Tokenize the Dataset"
]
},
{
"cell_type": "markdown",
"source": [
"### Run one of the following cell if loading from local. Otherwise x_train and y_train are already defined."
],
"metadata": {
"id": "3UMlgohccAPg"
}
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"X_train = pd.read_csv('X_train.csv', index_col=0)\n",
"y_train = pd.read_csv('y_train.csv', index_col=0)"
],
"metadata": {
"id": "z2BM318ufee_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train_encodings = torch.load('train_encodings.pt')\n",
"val_encodings = torch.load('val_encodings.pt')\n",
"test_encodings = torch.load('test_encodings.pt')"
],
"metadata": {
"id": "sVTr9fxIMZl9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Otherwise, Continue running here"
],
"metadata": {
"id": "j0FyKqdMezWv"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "U09fvCzaMn2P"
},
"outputs": [],
"source": [
"# Dictionary of feature names to use in the make sentence function\n",
"feature_names = {'id.resp_p':'response port',\n",
" 'proto_label':'transport protocol',\n",
" 'orig_pkts':'number of packets sent by the origin',\n",
" 'conn_state_label':'connection state',\n",
" 'orig_ip_bytes':'number of IP level bytes sent by the originator',\n",
" 'resp_ip_bytes':'number of IP level bytes sent by the responder'}\n",
"\n",
"# Function to make sentences out of the data\n",
"def make_sentence(row):\n",
" sentences = {}\n",
" for feature in row.keys():\n",
" if feature != 'label':\n",
" sentences[feature] = feature_names[feature] + \" is \" + str(row[feature]) + \".\"\n",
" return sentences"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Fe_vj8hO9dNw"
},
"outputs": [],
"source": [
"# Take all sentence observations and make them into paragraph inputs\n",
"def make_paragraphs(ser):\n",
" paragraphs_list = []\n",
" for index,obs in ser.items():\n",
" new_para = obs['id.resp_p'] + \" \" + obs['proto_label'] + \" \" + obs['conn_state_label'] + \" \" + obs['orig_pkts'] + \" \" + obs['orig_ip_bytes'] + \" \" + obs['resp_ip_bytes']\n",
" paragraphs_list.append(new_para)\n",
" return pd.Series(paragraphs_list, index=ser.index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bNyv9zOlGaBm"
},
"outputs": [],
"source": [
"from transformers import BertTokenizer\n",
"tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")\n",
"\n",
"\n",
"# Transform the dataset into sentences\n",
"X_train_sentences = X_train.apply(make_sentence, axis=1)\n",
"X_val_sentences = X_val.apply(make_sentence, axis=1)\n",
"X_test_sentences = X_test.apply(make_sentence, axis=1)\n",
"\n",
"# Transform the sentences into paragraphs\n",
"X_train_paragraphs = make_paragraphs(X_train_sentences)\n",
"X_val_paragraphs = make_paragraphs(X_val_sentences)\n",
"X_test_paragraphs = make_paragraphs(X_test_sentences)\n",
"\n",
"# Turn labels into lists of strings\n",
"y_train_str = [str(y) for y in y_train['label'].tolist()]\n",
"y_val_str = [str(y) for y in y_val['label'].tolist()]\n",
"y_test_str = [str(y) for y in y_test['label'].tolist()]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f5bT1RIEW0O7"
},
"outputs": [],
"source": [
"import torch\n",
"# Encode both paragraphs and the labels\n",
"train_encodings = tokenizer(text=X_train_paragraphs.tolist(), padding='longest', truncation=True, return_tensors='pt')\n",
"val_encodings = tokenizer(text=X_val_paragraphs.tolist(), padding='longest', truncation=True, return_tensors='pt')\n",
"test_encodings = tokenizer(text=X_test_paragraphs.tolist(), padding='longest', truncation=True, return_tensors='pt')\n",
"\n",
"# Add label tensors\n",
"y_train_tensor = torch.tensor(y_train['label'].values)\n",
"y_val_tensor = torch.tensor(y_val['label'].values)\n",
"y_test_tensor = torch.tensor(y_test['label'].values)\n",
"\n",
"train_encodings['labels'] = y_train_tensor\n",
"val_encodings['labels'] = y_val_tensor\n",
"test_encodings['labels'] = y_test_tensor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OV600RIVGlTi"
},
"outputs": [],
"source": [
"torch.save(train_encodings, 'train_encodings.pt')\n",
"torch.save(val_encodings, 'val_encodings.pt')\n",
"torch.save(test_encodings, 'test_encodings.pt')"
]
},
{
"cell_type": "markdown",
"source": [
"# Finally, prepare dataset as Hugging Face Dataset"
],
"metadata": {
"id": "gev2VE5VcnaY"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZNmaJOCUifpD"
},
"source": [
"### Optional: Load training, validation, and test encodings in from Drive or local"
]
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"id": "7NlSBStpD_rO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install torch==2.1.0\n",
"!pip install -U transformers[torch]\n",
"!pip install optimum[exporters]"
],
"metadata": {
"id": "okamUGSAmBYN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from transformers import BertTokenizer\n",
"# Load tensor data back from drive\n",
"train_encodings = torch.load(\"/content/drive/MyDrive/CS513 Final Project/Resources/train_encodings.pt\")\n",
"val_encodings = torch.load(\"/content/drive/MyDrive/CS513 Final Project/Resources/val_encodings.pt\")\n",
"test_encodings = torch.load(\"/content/drive/MyDrive/CS513 Final Project/Resources/test_encodings.pt\")\n",
"\n",
"# Load labels tensors back from drive\n",
"# y_train_tensor = torch.load(\"/content/drive/MyDrive/CS513 Final Project/Resources/y_train_tensor.pt\")\n",
"# y_val_tensor = torch.load(\"/content/drive/MyDrive/CS513 Final Project/Resources/y_val_tensor.pt\")\n",
"# y_test_tensor = torch.load(\"/content/drive/MyDrive/CS513 Final Project/Resources/y_test_tensor.pt\")"
],
"metadata": {
"id": "rVEX0OhgEAJT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# FROM LOCAL\n",
"import torch\n",
"train_encodings = torch.load(\"train_encodings.pt\")\n",
"val_encodings = torch.load(\"val_encodings.pt\")\n",
"test_encodings = torch.load(\"test_encodings.pt\")"
],
"metadata": {
"id": "Jxbp-oouNHsT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(train_encodings['input_ids'].size())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YY7xwbuZlhK4",
"outputId": "3faf0705-93f8-456e-8dbf-22b406314766"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"torch.Size([8000, 67])\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"### Otherwise, continue running here"
],
"metadata": {
"id": "dlTL3uj1fKF6"
}
},
{
"cell_type": "code",
"source": [
"# Creating small datasets to test finetuning\n",
"train = train_encodings\n",
"eval = val_encodings\n",
"test = test_encodings\n",
"\n",
"# Creating small datasets to test finetuning (delete :1000 for full dataset)\n",
"#train = train_encodings[:1000]\n",
"#eval = val_encodings[:1000]\n",
"#test = test_encodings[:1000]\n",
"\n",
"# Replacing target tensors (delete :128 for full label tensors)\n",
"# train['labels'] = y_train_tensor[:1000]\n",
"# eval['labels'] = y_val_tensor[:1000]\n",
"# test['labels'] = y_test_tensor[:1000]\n",
"\n",
"# Pytorch tensors to HF Dataset\n",
"from datasets import Dataset\n",
"train_dataset = Dataset.from_dict(train)\n",
"eval_dataset = Dataset.from_dict(eval)\n",
"test_dataset = Dataset.from_dict(test)"
],
"metadata": {
"id": "llZN2akWHxe5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "SRLNGcQFvJAa"
},
"source": [
"# Fine-tune BERT for benign vs malicious"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xf2CGlW1dLlH"
},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"from transformers import BertTokenizer, BertForSequenceClassification, AdamW, get_linear_schedule_with_warmup\n",
"from transformers import Trainer, TrainingArguments\n",
"from torch.utils.data import DataLoader, TensorDataset, random_split\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.utils.class_weight import compute_class_weight"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import evaluate\n",
"\n",
"combined_metrics = evaluate.combine([\"accuracy\", \"f1\"])"
],
"metadata": {
"id": "maPzffCsAS__"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def compute_metrics(eval_pred):\n",
" logits, labels = eval_pred\n",
" predictions = np.argmax(logits, axis=-1)\n",
" results = combined_metrics.compute(predictions=predictions, references=labels)\n",
" print(f\"Accuracy: {results['accuracy']:.3f}% | F1: {results['f1']:.3f}\")\n",
" return results"
],
"metadata": {
"id": "Subi5OZxAvlh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load pretrained BERT model\n",
"model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
"\n",
"# OR Load local model\n",
"# model = BertForSequenceClassification.from_pretrained('./model', num_labels=2)"
],
"metadata": {
"id": "OWLPaQn9ysMg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Define TrainingArguments\n",
"training_args = TrainingArguments(\n",
" output_dir='./results',\n",
" num_train_epochs=6,\n",
" per_device_train_batch_size=32,\n",
" # per_device_eval_batch_size=16,\n",
" warmup_steps=500,\n",
" weight_decay=0.01,\n",
" logging_dir='./logs',\n",
" # logging_steps=0.10,\n",
" eval_steps=0.10,\n",
" save_steps=0.10,\n",
" logging_strategy='epoch',\n",
" evaluation_strategy='epoch',\n",
" save_strategy='epoch',\n",
" save_total_limit=2,\n",
" load_best_model_at_end=True\n",
")\n",
"\n",
"# Create Trainer instance\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=eval_dataset,\n",
" compute_metrics=compute_metrics\n",
")\n",
"\n",
"# Train\n",
"trainer.train()"
],
"metadata": {
"id": "7a-zvoP0j8C8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(test_dataset)"
],
"metadata": {
"id": "SzDiVYRf23dp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TlxpJByQXL_w"
},
"outputs": [],
"source": [
"# Use test_dataset instead to test it later\n",
"trainer.evaluate(eval_dataset=test_dataset)"
]
},
{
"cell_type": "code",
"source": [
"model.save_pretrained('./model')"
],
"metadata": {
"id": "dqMkv8aA5Tdk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Save to Hugging Face"
],
"metadata": {
"id": "-qKGOqJTWt3a"
}
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import create_repo"
],
"metadata": {
"id": "m0mCacsshEhy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install cupy --upgrade"
],
"metadata": {
"id": "Ba-kOs8WqQTl"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"libcuda.so.1"
],
"metadata": {
"id": "AK2rcA5-qyGh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install onnxruntime\n",
"import onnxruntime as rt\n",
"import onnx\n",
"import cv2"
],
"metadata": {
"id": "8z2pir6uo-vM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!optimum-cli export onnx --model ./ --task question-answering ./results/checkpoint-10"
],
"metadata": {
"id": "WlderhErraWX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from onnxruntime import ORTModelForSequenceClassification\n",
"\n",
"ort_model = ORTModelForSequenceClassification.from_pretrained(model, export=True)\n",
"\n",
"ort_model.save_pretrained(\"./results/checkpoint-10\")"
],
"metadata": {
"id": "NzPr5eIkZfi7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Export model\n",
"import torch\n",
"# Get input ids\n",
"input_ids = train_dataset['input_ids']\n",
"# Convert to torch tensor\n",
"input_ids = torch.tensor(input_ids)\n",
"\n",
"torch.onnx.export(model, # Model being run\n",
" input_ids, # Model input\n",
" \"IoT23_Log_Prediction.onnx\",# Where to save the model\n",
" export_params=True, # Store model parameters\n",
" output_names=['labels'],\n",
" opset_version=11, # ONNX version\n",
" do_constant_folding=True, # Optimize\n",
" input_names = ['input_ids'])"
],
"metadata": {
"id": "ZM8xTkjeTm0c"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [
"td-xtcTdcoVO",
"GUB8N3k9fq-E",
"9lyEyWBic5RN",
"wRjakUpXD3D9",
"oO9g2nhlbr3o",
"3UMlgohccAPg",
"gev2VE5VcnaY",
"ZNmaJOCUifpD",
"L0eqXeQUTpXM"
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |