{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "rqX9hLfEziis" }, "source": [ "Further documentation for the libraries used can be found at https://unsloth.ai/" ] }, { "cell_type": "markdown", "metadata": { "id": "9eJjsg0_D2Dj" }, "source": [ "For information on hugging face tokens go to https://huggingface.co/docs/hub/en/security-tokens" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pdymVP95XA4_", "outputId": "d4095a9a-00b7-45a7-d268-055a0536e9a3" }, "outputs": [], "source": [ "# Add your hugging face token to secret keys or store your huggingface token as an environment variable.\n", "# It is used to download or upload models to your account.\n", "from google.colab import userdata\n", "from huggingface_hub import login\n", "login(userdata.get('TOKEN'))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "cBEG5Pq1lG-G" }, "outputs": [], "source": [ "%%capture\n", "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", "!pip install huggingface_hub\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "\n", "# We have to check which Torch version for Xformers (2.3 -> 0.0.27)\n", "from torch import __version__; from packaging.version import Version as V\n", "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aRz4XCANNy8b", "outputId": "198de2aa-0c91-4960-8ebc-a0a9b773a89a" }, "outputs": [], "source": [ "from unsloth import FastLanguageModel\n", "import torch\n", "\n", "max_seq_length = 2048\n", "\n", "def load_model(model_name, max_seq_length=max_seq_length, dtype=None, load_in_4bit=True):\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name=model_name,\n", " max_seq_length=max_seq_length,\n", " dtype=dtype,\n", " load_in_4bit=load_in_4bit,\n", " )\n", " return model, tokenizer" ] }, { "cell_type": "markdown", "metadata": { "id": "2bW-xOYMw7iC" }, "source": [ "#Load the base model and attach the trained LoRa adapters." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 345, "referenced_widgets": [ "506a3ea7b707487bb9da113627f9350c", "001649043914499ca5f3f1e970956de3", "2f54edab212545b5bd69f9b6e1c13a3e", "241d514194364bba8aa9c768e6c12f69", "4433917569de4a849f5e36e7b6b992ca", "b4cddc3ec1ae481b94caf13dd6b1c19c", "888e287c51ed445a9c3f7dce22f88277", "8119a7120bd249dba9eaaff7fa1946cf", "0c5fa664008f4dab80b71ec97cc7d061", "af5e6c4c14184e8c8fa31fe38a1492d4", "172c2fb6eb094bc1a8174c785bc8f007", "afd36e3f63ee4e40a163c247b44b25b4", "8317a7550ce640838cafba3159692b3d", "db32e1f329784045a0c3794695f7d946", "ee1ef03625b0429895529f4cf45e4c09", "2b9d7946bd4b4599abd19d2783830b43", "bbc62deaee2e49ed835b53be585e4783", "f5f3e149e35c42c5a44dbc170cb3025b", "85acc8c2304649ca81fadebb8c37077f", "ebceebd157874b89afbda6872f7fb525", "951b54529f6d4b4aa776f1c0d8035baa", "1606aa79fc8d44e6b0d41b24f2717265", "ee5f579570724c629872e30fc6816207", "23a1c4c2c4d64a5eb0a44f692eb388fb", "d389576d917e4c47b711eea64ab37645", "4fab2f19885748e4b52778cf082ccebe", "a2396cb195af42959f5f5f8cf4c377f9", "10229fa04f2541c8954cebc70d47c1ed", "f52e1a09e7e846b4bb160f3b67ffca77", "89f8d9b432964783b3173f4b9963951d", "cc4119f142df4342be08d3280e868432", "5812f6a94da64860bfaf53c75513ee77", "6b9e74b724c34d1e86444f1a18ff7481", "2dc0b07a516b4dd1916a255706e0b10e", "457d924ac14a4e9ba93de4db8055ecf4", "65e45c0c212c4ba083e890dd9824114e", "4367c706e81b4e24b5479d51420e07d2", "5050f9f42b164953ad1c3a11c8a0df79", "b09da2a56a594a8885c2a64cb5c0406b", "be5754743fac42e5aba8929b281d8e20", "3c40f77622d94ede82d16d0cb7358dbb", "f5273471991348dda3e16ddfb4e228fa", "2bd19201cca24b559ed83295d9b98a01", "67b77810c5124894aa2d403319f26f92", "3a9f7e78b6e14111a827d631c0d91924", "16882388dcaf4b9abb1411b98b87dc5a", "e5ce3be03d9a406881211344d66f8998", "9f87909fa7814527ba528fea7a40f0c0", "52d064af92e74285af7c8a043807621e", "6ca6c3b15c174334a1f52cf74e700b61", "dc84ad97dfb84b818669f4e95b22da75", "645db5e51b52468ca918dfefacee47d4", "e65925e463f84b88ba9abcb73b10d6a3", "99ccb950387b41bfb6a300c67cf25cea", "d2cb177ee6b54d379d57c42f734dab99", "82c9911d402e4372add9b2851b421a84", "f6f9c9d581be4b528fcb02c9308b0a5d", "55ffb7cd962b4fada9281c0197b73796", "ec127f4e473245c4ba04c6ab8968ba72", "b6b83b9dc9324b6d978a5f4e65204e53", "4008ba4ddcf740a28483f2d84c96b312", "c58b5ba165f94ae0b6591a7948b36c08", "62aeae6fe942461caf12f90321d641fd", "02ec339c61aa45d5bf6657bd54ecf142", "3fe8d851e0ff429789052513c426cf42", "d27b0ee3eb6248a2a4dc74985ced1ab0", "9e93e2c8bbe0482cbd4f9ffaceb51cca", "276aadb3ebc84203bacb6959f97c281e", "dd81c55d31f048be856a352ca60bf0f6", "6cf4a511e4d04b959faea957e07c0926", "99154a76fc32444d90db0f38c7747bed", "f86fa76f479a49d195f028ad4dfeccf5", "5970c81d60ef40e2a52e65bb467649f7", "36835d38f79f47efb48e8aee7a85a2a5", "1f48b6f6ff9c4ca68902f80bfc928cc2", "7573484708de4a4899535103de34706f", "af93c7eb78914919a56d231a56c82fe3" ] }, "id": "urDIg3QRlCMx", "outputId": "435a2e48-0da4-47d6-cd27-78ee7d6ee4fa" }, "outputs": [], "source": [ "from peft import PeftModel\n", "\n", "models = [\n", " 'Phi-3.5-mini-instruct-bnb-4bit', # |Min Training Gpu : T4, Min Testing GPU: T4, Max Model size : 14.748 GB|\n", " 'gemma-2-27b-it-bnb-4bit', # |Min Training Gpu: A100, Min Testing GPU: T4, Max Model size: 39.564GB|\n", " 'Meta-Llama-3.1-8B-Instruct-bnb-4bit' # |Min Training Gpu: T4, Min Testing GPU: T4, Max Model size : 22.168GB|\n", "]\n", "\n", "chat_templates = [\n", " 'phi-3',\n", " 'alpaca', #Or gemma.\n", " 'llama-3.1'\n", "]\n", "\n", "# Select model and corresponding template.\n", "selection = 0\n", "model_name = models[selection]\n", "chat_template = chat_templates[selection]\n", "HfUsername = \"CooperW\"\n", "\n", "try:\n", " # This is the name of your model repository on hugging face.\n", " LoRa_Adapters = f\"{HfUsername}/{model_name.replace('-', '_').replace('.', '_')}_128Batch\"\n", "except:\n", " print('No LoRa Adapters Found')\n", " exit()\n", "\n", "# Load base model and fit Adapters.\n", "model = f\"unsloth/{model_name}\"\n", "model, tokenizer = load_model(model)\n", "model = PeftModel.from_pretrained(model, LoRa_Adapters)" ] }, { "cell_type": "markdown", "metadata": { "id": "dcill7J8v7mV" }, "source": [ "#Data Mapping\n", "This function will take the given dataset and map the contents to the [\"text\"] field, this format is what is needed to be input to the model.\n", "\n", "Example:\n", "```\n", "<|user|>\n", "Hi!<|end|>\n", "<|assistant|>\n", "Hello! How are you?<|end|>\n", "<|user|>\n", "I'm doing great! And you?<|end|>\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "QVEYv-pnlh9s" }, "outputs": [], "source": [ "from unsloth.chat_templates import get_chat_template\n", "\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = chat_template,\n", " mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"},\n", ")\n", "\n", "def formatting_prompts_func(examples):\n", " convos = examples[\"conversations\"]\n", " texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n", " return { \"text\" : texts, }\n", "pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "e13163b41a0244189ea20b8f2c356d3c", "3d045401a8ed4287ac82959d90c412e1", "156654d7561941b18a1ea735f613df9d", "5ca8b1cbeb9b4f6996c4d07f3bd70c12", "3bac8f63af81416ea01ab113200c1bc6", "a120493dcb7f4ce3950e37a6f3780c71", "b7b2dbf8b7324a3d86ed4fc1c44c420c", "9ae67e29c34f4b168605b46ab6646148", "ea026875142041cc9e1f0963eee41fa5", "5c9c0896fc8240a6b82314b14a4de067", "c2cd7f17cef341c4bce1566af4160d6c", "04bf2b768a444057bfcebb2085577468", "1063075fb5c34c9caa26a4d6471c19ce", "21e8106fbd25486f86c846af89990a91", "c2c5a72f8c7141b88dd5dd462991e674", "fdeb98120cb840eaa6874d0d6ee22715", "e880a92081b04ef49bad678b460032da", "e305a0f571114f04a2844cc9af2cca9c", "6a1a0e95f98c41d4be8b19e4468f52ef", "e7ad33ce1bcc4bd2bd90b9736835f7d3", "acbb0fb4c96a4541be47185e669ba309", "b916fffd847142e197fd941937247834", "7fb266ed2bc04184849b03ec6d719cf7", "703e4e0506a7463c89524f53d5c2d0f1", "2367151020f54401bb3b2787c29c8bf7", "289ddcce0dbd43beb5558d21fc7830b2", "5276305ab50a46ceaa2cde0282f48d5e", "b356bfebddf44b4f8d4ed2eca8b85cd1", "e82aaf31dbeb4e7baa5d113c3444740a", "10c89da311974709af330beb52cc8c5b", "c64e4aa995b04e10979023b51b07e0e1", "ef9f210a7f23435b8988e3538044a254", "eb8cccb37e59425b843173e4164c9502" ] }, "id": "iVqRqJrqlpvD", "outputId": "d8151463-39c0-4c1f-a951-f42aad7d9265" }, "outputs": [], "source": [ "# This will load datasets from your hugging face repository, alternatively load locally stored datasets.\n", "from datasets import load_dataset\n", "\n", "dataset_repo = f\"{HfUsername}/jadidi\"\n", "dataset_name = \"test_network.jsonl\"\n", "\n", "test_dataset = load_dataset(dataset_repo, data_files=dataset_name, split='train')\n", "test_dataset = test_dataset.map(formatting_prompts_func, batched = True,)" ] }, { "cell_type": "markdown", "metadata": { "id": "hTj1u2o6vSSn" }, "source": [ "#Evaluation Loop\n", "This function will send an input message to the model, the input will be a row from the testing dataset then recieve an output, complete the output if necessary, and provide a live accuracy update." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "6N1GoeHFY_cC" }, "outputs": [], "source": [ "import time\n", "import sys\n", "\n", "def evaluate_model_on_dataset(model, tokenizer, test_dataset, num_samples=len(test_dataset)):\n", "\n", " # Enable native 2x faster inference\n", " FastLanguageModel.for_inference(model)\n", "\n", " # List to store predictions\n", " predictions = []\n", " ground_truths = []\n", " start_time = time.time()\n", "\n", " # Metrics\n", " correct = 0\n", " incorrect = 0\n", " accuracy = 0\n", "\n", " prompt = \"\"\n", "\n", " # Function to map incomplete answers to full words (e.g., attac or norm)\n", " def complete_prediction(pred):\n", " # Dictionary of known classes in the ToN-IoT dataset with their first letters (or first two for dos and ddos)\n", " known_classes = {\n", " \"n\": \"normal\", # Normal traffic\n", " \"m\": \"mitm\", # Man in the Middle attack\n", " \"p\": \"password\", # Password attacks (brute force, etc.)\n", " \"b\": \"backdoor\", # Backdoor attacks\n", " \"i\": \"injection\", # SQL injection, Command injection, etc.\n", " \"x\": \"xss\", # Cross-Site Scripting (XSS) attacks\n", " \"r\": \"ransomware\", # Ransomware\n", " \"s\": \"scanning\", # Port scanning, vulnerability scanning\n", " \"a\": \"attack\" # For Binary Classification\n", " }\n", "\n", " pred_lower = pred.lower() # Convert prediction to lowercase\n", "\n", " # For \"dos\" and \"ddos\", use the first two letters; for others, use the first letter\n", " if pred_lower.startswith(\"dd\"):\n", " return \"ddos\"\n", " elif pred_lower.startswith(\"do\"):\n", " return \"dos\"\n", " else:\n", " # Use the first letter for other classes\n", " return known_classes.get(pred_lower[0], pred) # Return original prediction if no match is found\n", "\n", " # Loop over each item in the test dataset\n", " for item in test_dataset.select(range(num_samples)):\n", "\n", " # Extract the human input from the item\n", " human_input = item['conversations'][0]['value']\n", "\n", " # Prepare messages with the current input\n", " message = [{\"from\": \"human\", \"value\": f\"{prompt}{human_input}\"}]\n", "\n", " # Tokenize the input\n", " inputs = tokenizer.apply_chat_template(\n", " message,\n", " tokenize=True,\n", " add_generation_prompt=True, # Must add for generation\n", " return_tensors=\"pt\",\n", " ).to(\"cuda\")\n", "\n", " # Generate the output\n", " outputs = model.generate(input_ids=inputs, max_new_tokens=1, use_cache=True)\n", " answer = tokenizer.batch_decode(outputs)\n", "\n", " # Process the prediction\n", " pred = complete_prediction(answer[0].split()[-1])\n", " truth = item['conversations'][1]['value'].strip()\n", "\n", " # Append predictions and truths\n", " predictions.append(pred)\n", " ground_truths.append(truth)\n", "\n", " # Update metrics\n", " if pred == truth:\n", " correct += 1\n", " color_code = \"\\033[32m\" # Green\n", " else:\n", " incorrect += 1\n", " color_code = \"\\033[31m\" # Red\n", "\n", " # Calculate accuracy\n", " elapsed_time = time.time() - start_time\n", " accuracy = (correct / (correct + incorrect)) * 100\n", "\n", " # Print current status\n", " sys.stdout.write(f\"\\rElapsed Time: {elapsed_time:.2f} seconds, Accuracy: {accuracy:.2f}%, Completed: {correct + incorrect}/{num_samples} Prediction: {color_code}{pred}\\033[0m, Ground Truth: {color_code}{truth}\\033[0m\")\n", " sys.stdout.flush()\n", "\n", " print(\"\\n\\n\\nInference Complete\\n\\n\\n\")\n", "\n", " return predictions, ground_truths" ] }, { "cell_type": "markdown", "metadata": { "id": "EtpHf7bTvH_h" }, "source": [ "#For Binary Classification Metrics" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "mVmdz7LNJ1MO" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n", "import numpy as np\n", "import pandas as pd\n", "\n", "def metrics(predictions, ground_truths):\n", " # Convert to numpy arrays\n", " y_true = np.array(ground_truths)\n", " y_pred = np.array(predictions)\n", "\n", " # Convert string labels to numeric values\n", " label_mapping = {'attack': 1, 'normal': 0}\n", " y_true_numeric = np.array([label_mapping[label] for label in y_true])\n", " y_pred_numeric = np.array([label_mapping[label] for label in y_pred])\n", "\n", " accuracy = accuracy_score(y_true, y_pred)*100\n", " print(f\"Accuracy: {accuracy:.2f}%\\n\")\n", "\n", " # Print classification report\n", " class_report = classification_report(y_true, y_pred, labels=['attack', 'normal'])\n", " print(\"Classification Report:\")\n", " print(class_report, '\\n')\n", "\n", " # Compute metrics\n", " precision = precision_score(y_true, y_pred, average=None, labels=['attack', 'normal'])\n", " recall = recall_score(y_true, y_pred, average=None, labels=['attack', 'normal'])\n", " f1 = f1_score(y_true, y_pred, average=None, labels=['attack', 'normal'])\n", "\n", " metrics_df = pd.DataFrame({\n", " 'Class': ['attack', 'normal'],\n", " 'Precision': precision,\n", " 'Recall': recall,\n", " 'F1 Score': f1\n", " })\n", "\n", " # Create subplots\n", " fig, axes = plt.subplots(1, 2, figsize=(18, 6)) # Two plots side by side\n", "\n", " # Plot precision, recall, and F1 score\n", " metrics_df.plot(x='Class', kind='bar', ax=axes[0])\n", " axes[0].set_title('Precision, Recall, and F1 Score by Class')\n", " axes[0].set_xlabel('Class')\n", " axes[0].set_ylabel('Score')\n", " axes[0].set_ylim(0, 1)\n", "\n", " # Plot confusion matrix\n", " cm = confusion_matrix(y_true, y_pred, labels=['attack', 'normal'])\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['attack', 'normal'], yticklabels=['attack', 'normal'], ax=axes[1])\n", " axes[1].set_xlabel('Predicted')\n", " axes[1].set_ylabel('True')\n", " axes[1].set_title('Confusion Matrix')\n", "\n", " # Adjust layout\n", " plt.subplots_adjust(wspace=0.3)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "thGuQIEPYJog", "outputId": "c23b6086-9adf-4948-b86b-c716a4888d9b" }, "outputs": [], "source": [ "# Call the evaluation function\n", "pred, truth = evaluate_model_on_dataset(model, tokenizer, test_dataset, num_samples=1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IGfDpQJCwQFA" }, "outputs": [], "source": [ "metrics(pred, truth)" ] }, { "cell_type": "markdown", "metadata": { "id": "2vagHjHGvEK8" }, "source": [ "#For Multi-Class Classification Metrics" ] }, { "cell_type": "markdown", "metadata": { "id": "j1M3mCf8fogx" }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3w4hCfkuC6G3" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n", "import numpy as np\n", "import pandas as pd\n", "\n", "def metrics(predictions, ground_truths):\n", " # Convert to numpy arrays\n", " y_true = np.array(ground_truths)\n", " y_pred = np.array(predictions)\n", "\n", " # List of all known ToN-IoT classes\n", " class_labels = [\"normal\", \"ddos\", \"dos\", \"mitm\", \"password\", \"backdoor\", \"injection\", \"xss\", \"ransomware\", \"scanning\"]\n", "\n", " # Compute and print accuracy\n", " accuracy = accuracy_score(y_true, y_pred) * 100\n", " print(f\"Accuracy: {accuracy:.2f}%\\n\")\n", "\n", " # Print classification report\n", " class_report = classification_report(y_true, y_pred, labels=class_labels, zero_division=0)\n", " print(\"Classification Report:\")\n", " print(class_report, '\\n')\n", "\n", " # Compute precision, recall, and F1 score for each class\n", " precision = precision_score(y_true, y_pred, average=None, labels=class_labels, zero_division=0)\n", " recall = recall_score(y_true, y_pred, average=None, labels=class_labels, zero_division=0)\n", " f1 = f1_score(y_true, y_pred, average=None, labels=class_labels, zero_division=0)\n", "\n", " metrics_df = pd.DataFrame({\n", " 'Class': class_labels,\n", " 'Precision': precision,\n", " 'Recall': recall,\n", " 'F1 Score': f1\n", " })\n", "\n", " # Create subplots\n", " fig, axes = plt.subplots(1, 2, figsize=(18, 6)) # Two plots side by side\n", "\n", " # Plot precision, recall, and F1 score\n", " metrics_df.plot(x='Class', kind='bar', ax=axes[0], color=['blue', 'orange', 'green'])\n", " axes[0].set_title('Precision, Recall, and F1 Score by Class')\n", " axes[0].set_xlabel('Class')\n", " axes[0].set_ylabel('Score')\n", " axes[0].set_ylim(0, 1)\n", " axes[0].legend([\"Precision\", \"Recall\", \"F1 Score\"], loc='upper left')\n", "\n", " # Plot confusion matrix\n", " cm = confusion_matrix(y_true, y_pred, labels=class_labels)\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels, ax=axes[1])\n", " axes[1].set_xlabel('Predicted')\n", " axes[1].set_ylabel('True')\n", " axes[1].set_title('Confusion Matrix')\n", "\n", " # Adjust layout\n", " plt.subplots_adjust(wspace=0.3)\n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jqJ56550huU2" }, "outputs": [], "source": [ "metrics(pred, truth)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hbI9xSd65zdg" }, "outputs": [], "source": [ "# from google.colab import runtime\n", "# runtime.unassign()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "L4", "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "001649043914499ca5f3f1e970956de3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": 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