Paper outlines a seven-agent n8n workflow for log forensics
Original title: An Agentic Multi-Agent Framework for System Log Anomaly Detection
Agents built on n8n and Gemini pass results layer by layer; authors tested 16 cases and call the findings promising.
A cs.CR paper proposes a multi-agent framework for system-log anomaly detection and attack forensics. It is built on the n8n platform and the Gemini large language model, with seven agent layers that each handle one task and forward results onward. The authors say the design can handle many log-analysis requests at once. They report deploying 16 test cases and evaluating accuracy and operative performance, but give no figures.
Unconfirmed: The authors call the findings very promising without reporting measured accuracy.
- The workflow has seven AI agent layers.
- Each agent forwards completed work to the next layer.
- Implementation uses n8n and Gemini.
- Evaluation used 16 test cases.
- The paper does not state accuracy or latency numbers.
From the original article
1 min read at arxiv.orgAs enterprise systems grow in scale and complexity, manual log analysis and conventional SIEM tools struggle to keep pace with the volume and diversity of incidents. To address this challenge, this paper proposed a multi-agent AI framework supporting in analyzing the system log to trace the attack forensic in the system more effectively and efficiently.
By Ngoc-Son Le · arxiv.org
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