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a domain is generated by a Domain Generation Algorithm (DGA) or is a legitimate domain. We use a large language model (LLM) fine-tuned specifically for domain classification tasks.\n", "\n", "## Features of the Code\n", "\n", "- **Text Generation**: Uses a language model to generate labels based on a specific prompt.\n", "- **Automatic Classification**: Determines whether a domain belongs to a DGA based on the generated labels.\n", "- **Modular Structure**: The code is optimized with a reusable function to efficiently evaluate multiple domains.\n", "\n", "## Code Structure\n", "\n", "### Environment Setup:\n", "- Loads the language model via the Hugging Face `transformers` library.\n", "- Initializes the text generation function.\n", "\n", "### Defining the `evaluate_domain` Function:\n", "- The function takes a domain and a text generator as input.\n", "- It generates a customized prompt for the given domain.\n", "- Processes the model's output to classify the domain as:\n", " - `1`: If the domain is generated by a DGA.\n", " - `0`: If the domain is legitimate.\n", "\n", "### Testing and Usage:\n", "- Practical examples showing how to evaluate domains like `google.com` or `gfdjgerenyqsgert.com`.\n", "\n", "## Example Usage\n", "\n", "```python\n", "# Evaluate domains\n", "domain1 = \"google.com\"\n", "domain2 = \"gfdjgerenyqsgert.com\"\n", "\n", "label1 = evaluate_domain(generator, domain1)\n", "label2 = evaluate_domain(generator, domain2)\n", "\n", "print(f\"Domain: {domain1}, Label: {label1}\")\n", "print(f\"Domain: {domain2}, Label: {label2}\")\n", "```\n", "\n", "##Applications\n", "This system can be used in cybersecurity tasks to:\n", "\n", "- Identify malicious domains generated by malware.\n", "\n", "- Enhance DNS-based threat detection systems.\n", "\n", "- Automate real-time domain list analysis.\n", "\n", "\n", "##Hardware Requirements\n", "This code was run on a Google Colab GPU T4 with 16GB of memory, but the minimum requirement for running it is a GPU with 8GB of memory." ], "metadata": { "id": "2dgRyVT9mkRH" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "NCsBtyp5bN7c" }, "outputs": [], "source": [ "%%capture\n", "!pip install torch datasets transformers trl huggingface_hub pandas peft bitsandbytes" ] }, { "cell_type": "code", "source": [ "import torch\n", "import os\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n", "from peft import PeftModel # Importing the custom PeftModel\n", "\n", "from transformers import BitsAndBytesConfig # Importing configuration for Bits and Bytes quantization\n", "\n", "from google.colab import drive # Importing the library to mount Google Drive\n", "drive.mount('/content/drive') # Mounting Google Drive in Colab environment\n", "\n", "from huggingface_hub import login # Importing login function from Hugging Face Hub\n", "\n", "login(token=\"hf_RQLxVGKFBgcLsMSzNTY......\") # Logging into Hugging Face Hub with a specific token\n", "\n", "# Path to the adapter files on Google Drive\n", "adapter_dir = '/content/drive/My Drive/adapter' # Defining the path where adapter files are located\n", "\n", "# Configuration for 4-bit quantization\n", "compute_dtype = getattr(torch, \"float16\") # Calculation data type used for quantization (float16 in this case)\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True, # Load in 4-bit quantization\n", " bnb_4bit_quant_type=\"nf4\", # Type of 4-bit quantization used\n", " bnb_4bit_compute_dtype=compute_dtype, # Calculation data type for 4-bit quantization\n", " bnb_4bit_use_double_quant=True, # Use double quantization for 4 bits\n", ")\n", "\n", "# Load pretrained model with quantization\n", "model_name = \"meta-llama/Meta-Llama-3-8B\" # Name of the pretrained model to use\n", "model = AutoModelForCausalLM.from_pretrained(\n", " model_name,\n", " quantization_config=bnb_config, # Quantization configuration applied to the loaded model\n", " device_map={\"\": 0} # Device mapping, in this case, the model is loaded on device 0\n", ")\n", "\n", "# Load the adapter\n", "model = 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Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/654 [00:00