Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning
arXiv:2606.08649v1 Announce Type: cross Abstract: Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements. We present CEF-Log, a context-enhanced few-shot chain-of-thought prompting strategy for Large Language Models that addresses this dual requirement. CEF-Log embeds expert investigative methodology through a structured five-step reasoning template, enabling the model to learn \textit{how} to analyze logs rather than \textit{what} patterns to memorize. Experimental evaluation demonstrates that CEF-Log achieves a
The increasing sophistication of cyber threats and the widespread adoption of AI for defense are driving the need for more intelligent and explainable security solutions.
This development addresses critical challenges in cybersecurity by providing explainable AI-driven forensic analysis, which is crucial for legal compliance and effective threat response.
Traditional cybersecurity forensics, often relying on manual review or black-box AI, can now potentially integrate AI that not only detects but also articulates its reasoning in human-understandable terms.
- · Cybersecurity firms
- · Law enforcement agencies
- · Organizations with stringent compliance requirements
- · AI-powered security solution providers
- · Malicious actors undetected by legacy systems
- · Organizations with poor log management practices
Improved detection rates and reduced investigation times for web server log analysis.
Increased trust in AI-driven cybersecurity tools due to enhanced explainability and forensic utility.
Potential for new legal precedents or standards around AI-generated evidence in digital forensics.
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Read at arXiv cs.AI