Authors: Hangyuan Ji, Jian Yang, Linzheng Chai, Chaoren Wei, Liqun Yang, Yunlong Duan, Yunli Wang, Tianzhen Sun, Hongcheng Guo, Tongliang Li, Changyu Ren, Zhoujun Li

Published on: May 06, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2405.03446

Summary

• What is new: Introduction of SEvenLLM framework for enhancing cybersecurity LLMs using a high-quality bilingual instruction corpus and multi-task learning.
• Why this is important: The increasing complexity and frequency of cybersecurity incidents with over 10 billion instances reported recently.
• What the research proposes: A framework (SEvenLLM) that uses large language models to analyze and respond to cybersecurity incidents more effectively by training on a specialized instruction dataset (SEvenLLM-Instruct).
• Results: SEvenLLM demonstrates improved threat analysis and stronger defenses against cyber threats as shown in experiments on the SEvenLLM-bench benchmark.

Technical Details

Technological frameworks used: SEvenLLM, a large language model framework specifically for Security Events

Models used: Large language models trained on a specialized multi-task learning objective

Data used: A high-quality bilingual instruction corpus created from cybersecurity websites

Potential Impact

Cybersecurity service providers and software vendors could significantly benefit from the insights, potentially disrupting the current market by introducing more advanced, AI-driven cybersecurity solutions.

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