Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study

• URL: http://arxiv.org/abs/2503.18487v2
• Date: Mon, 23 Jun 2025 09:35:39 GMT
• Title: Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study
• Authors: Xinggong Zhang, Haotian Meng, Qingyang Li, Yunpeng Tan, Lei Zhang,
• Abstract summary: Large Language Models (LLMs) are trained on a vast corpus of text.<n>We focus on unleashing the full potential of LLMs in malicious traffic detection.<n>We present our design on LLM-powered DDoS detection as a case study.
• Score: 12.381768120279771
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Malicious traffic detection is a pivotal technology for network security to identify abnormal network traffic and detect network attacks. Large Language Models (LLMs) are trained on a vast corpus of text, have amassed remarkable capabilities of context-understanding and commonsense knowledge. This has opened up a new door for network attacks detection. Researchers have already initiated discussions regarding the application of LLMs on specific cyber-security tasks. Unfortunately, there remains a lack of comprehensive analysis on harnessing LLMs for traffic detection, as well as the opportunities and challenges. In this paper, we focus on unleashing the full potential of Large Language Models (LLMs) in malicious traffic detection. We present a holistic view of the architecture of LLM-powered malicious traffic detection, including the procedures of Pre-training, Fine-tuning, and Detection. Especially, by exploring the knowledge and capabilities of LLM, we identify three distinct roles LLM can act in traffic classification: Classifier, Encoder, and Predictor. For each of them, the modeling paradigm, opportunities and challenges are elaborated. Finally, we present our design on LLM-powered DDoS detection as a case study. The proposed framework attains accurate detection on carpet bombing DDoS by exploiting LLMs' capabilities in contextual mining. The evaluation shows its efficacy, exhibiting a nearly 35% improvement compared to existing systems.

Related papers

• Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions [7.478562842905953]
  Large Language Models (LLMs) have revolutionized various fields with their exceptional capabilities in understanding, processing, and generating human-like text.<n>This paper investigates the potential of LLMs in advancing Network Intrusion Detection Systems (NIDS), analyzing current challenges, methodologies, and future opportunities.
  arXiv  Detail & Related papers  (2025-07-07T08:28:07Z)
• A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Case [59.58213261128626]
  We propose a blockchain-enabled collaborative framework that connects multiple Large Language Models (LLMs) into a Trustworthy Multi-LLM Network (MultiLLMN)<n>This architecture enables the cooperative evaluation and selection of the most reliable and high-quality responses to complex network optimization problems.
  arXiv  Detail & Related papers  (2025-05-06T05:32:46Z)
• Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap [51.198001060683296]
  Large Language Models (LLMs) offer transformative potential to address transportation challenges.<n>This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation.<n>For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization.
  arXiv  Detail & Related papers  (2025-03-27T11:56:27Z)
• Knowledge Transfer from LLMs to Provenance Analysis: A Semantic-Augmented Method for APT Detection [1.2571354974258824]
  We propose a new strategy for taking advantage of Large Language Models (LLMs) in provenance-based threat detection. LLMs offer additional details in provenance data interpretation, leveraging their knowledge of system calls, software identity, and high-level understanding of application execution context. In our evaluation, supervised threat detection achieves a precision of 99.0%, and semi-supervised anomaly detection attains a precision of 96.9%.
  arXiv  Detail & Related papers  (2025-03-24T03:51:09Z)
• Attention Tracker: Detecting Prompt Injection Attacks in LLMs [62.247841717696765]
  Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks. We introduce the concept of the distraction effect, where specific attention heads shift focus from the original instruction to the injected instruction. We propose Attention Tracker, a training-free detection method that tracks attention patterns on instruction to detect prompt injection attacks.
  arXiv  Detail & Related papers  (2024-11-01T04:05:59Z)
• Detecting and Understanding Vulnerabilities in Language Models via Mechanistic Interpretability [44.99833362998488]
  Large Language Models (LLMs) have shown impressive performance across a wide range of tasks. LLMs in particular are known to be vulnerable to adversarial attacks, where an imperceptible change to the input can mislead the output of the model. We propose a method, based on Mechanistic Interpretability (MI) techniques, to guide this process.
  arXiv  Detail & Related papers  (2024-07-29T09:55:34Z)
• A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends [78.3201480023907]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks. The vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage. In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks.
  arXiv  Detail & Related papers  (2024-07-10T06:57:58Z)
• AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models [95.09157454599605]
  Large Language Models (LLMs) are becoming increasingly powerful, but they still exhibit significant but subtle weaknesses. Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies. We introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks.
  arXiv  Detail & Related papers  (2024-06-24T15:16:45Z)
• Towards Explainable Vulnerability Detection with Large Language Models [17.96542494363619]
  Software vulnerabilities pose significant risks to the security and integrity of software systems.<n>The advent of large language models (LLMs) has introduced transformative potential due to their advanced generative capabilities.<n>In this paper, we propose LLMVulExp, an automated framework designed to specialize LLMs for the dual tasks of vulnerability detection and explanation.
  arXiv  Detail & Related papers  (2024-06-14T04:01:25Z)
• Large Language Models for Cyber Security: A Systematic Literature Review [14.924782327303765]
  We conduct a comprehensive review of the literature on the application of Large Language Models in cybersecurity (LLM4Security) We observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training.
  arXiv  Detail & Related papers  (2024-05-08T02:09:17Z)
• Can LLMs Understand Computer Networks? Towards a Virtual System Administrator [15.469010487781931]
  This paper is the first to conduct an exhaustive study on Large Language Models' comprehension of computer networks. We evaluate our framework on multiple computer networks employing proprietary (e.g., GPT4) and open-source (e.g., Llama2) models.
  arXiv  Detail & Related papers  (2024-04-19T07:41:54Z)
• A Survey on Detection of LLMs-Generated Content [97.87912800179531]
  The ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks. We also posit the necessity for a multi-faceted approach to defend against various attacks.
  arXiv  Detail & Related papers  (2023-10-24T09:10:26Z)
• A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks [28.1095109118807]
  Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks. LLMs may be exposed to maliciously manipulated training data and processing, providing an opportunity for attackers to embed a hidden backdoor into the model. Backdoor attacks are particularly concerning within communication networks where reliability and security are paramount.
  arXiv  Detail & Related papers  (2023-08-28T07:31:43Z)
• Red Teaming Language Model Detectors with Language Models [114.36392560711022]
  Large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. Recent works have proposed algorithms to detect LLM-generated text and protect LLMs. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation.
  arXiv  Detail & Related papers  (2023-05-31T10:08:37Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.