Authors: HanXiang Xu, ShenAo Wang, Ningke Li, Yanjie Zhao, Kai Chen, Kailong Wang, Yang Liu, Ting Yu, HaoYu Wang

Published on: May 08, 2024

Impact Score: 8.2

Arxiv code: Arxiv:2405.04760

Summary

• What is new: This research provides a thorough review of the application of Large Language Models (LLMs) in cybersecurity, highlighting new techniques and identifying key challenges and opportunities for future work in this area.
• Why this is important: The increasing volume and sophistication of cyber threats require intelligent systems for automatic vulnerability detection, malware analysis, and attack response.
• What the research proposes: A comprehensive survey of over 30,000 papers to analyze how LLMs are applied to cybersecurity tasks, with a focus on vulnerability detection, malware analysis, network intrusion detection, and phishing detection.
• Results: Key findings include the wide range of cybersecurity tasks LLMs are applied to, the limitation of datasets in diversity and size, promising techniques for LLM adaptation like fine-tuning and transfer learning, and the identified needs for future research including interpretability and data privacy.

Technical Details

Technological frameworks used: Fine-tuning, transfer learning, domain-specific pre-training

Models used: Large Language Models (LLMs)

Data used: Over 30,000 relevant papers and systematically analyzed 127 papers

Potential Impact

Cybersecurity service providers, cybersecurity tool developers, companies investing in AI for threat detection and prevention

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