LLM method improves malicious log detection with explainable reasoning

Researchers have developed a new method called CEF-Log for using Large Language Models to detect malicious web server logs. This approach uses a structured five-step reasoning template to guide the LLM, improving its ability to analyze logs and generate legally sound explanations. CEF-Log demonstrated high accuracy with minimal examples, achieving an F1-score of 0.99 on a known dataset and showing a tenfold increase in sample efficiency compared to other methods. A new dataset, ForenWebLog, was also introduced to evaluate the system on more complex, real-world attack scenarios. AI

IMPACT Enhances LLM capabilities in cybersecurity by enabling sample-efficient and explainable detection of malicious activities.

RANK_REASON Academic paper detailing a new method for log analysis using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• CEF-Log
• CSIC 2010
• ForenWebLog
• Hong-Hanh Nguyen-Le
• Large Language Models

• paper
• safety

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