{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/trafficgpt-an-llm-approach-for-open-set","title":"TrafficGPT: An LLM Approach for Open-Set Encrypted Traffic Classification","arxiv_id":null,"date":"2024-08-06","proceeding":"AINTEC 2024 8","authors":["Yasod Ginige","Thilini Dahanayaka","Suranga Seneviratne"],"abstract":"Encrypted traffic has been known to be vulnerable to traffic analysis attacks that exploit the statistical features of encrypted traffic flows, such as packet sizes, timing, and direction, to infer information about the underlying content, which undermines the privacy guarantees of end-to-end encryption. While state-of-the-art attacks leverage deep learning models to achieve high accuracy, most attacks work under the less realistic closed-set assumption. Deploying such attacks in practice requires addressing the open-set scenario, which allows the models to filter out target content from other background traffic. Concurrently, Large Language Models (LLM) are increasingly gaining traction due to their ability to adapt to diverse tasks in domains outside NLP, especially in applications with sequential data. Inspired by this, our work introduces TrafficGPT, a novel traffic analysis attack that leverages GPT-2, a popular LLM, to enhance feature extraction, thereby improving the open-set performance of downstream classification. We use five existing encrypted traffic datasets to show how the feature extraction by GPT-2 improves the open-set performance of traffic analysis attacks compared to ET-BERT and CNN-based approaches by 12.7% and 13.7%, respectively.","url_abs":"https://dl.acm.org/doi/abs/10.1145/3674213.3674217","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3674213.3674217","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"trafficgpt-an-llm-approach-for-open-set","repo_url":"https://github.com/YasodGinige/TrafficGPT","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"traffic-classification","task_name":"Traffic Classification"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"discriminative-fine-tuning","method_name":"Discriminative Fine-Tuning"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt-2","method_name":"GPT-2"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}