LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis - Juyoung Lee; Yeonsu Jeong; Taehyun Han; Taejin Lee (Applied Sciences, 2025)
As cyber threats become increasingly sophisticated, existing log-based anomaly detection models face critical limitations in adaptability, semantic interpretation, and operational automation. Traditional approaches based on CNNs, RNNs, and LSTMs struggle with inconsistent log formats and often lack interpretability. To address these challenges, we propose LogRESP-Agent, a modular AI framework built around a reasoning-based agent for log-driven security prediction and response. The architecture integrates three core capabilities, including (1) LLM-based anomaly detection with semantic explanation, (2) contextual threat reasoning via Retrieval-Augmented Generation (RAG), and (3) recursive investigation capabilities enabled by a planning-capable LLM agent. This architecture supports automated, multi-step analysis over heterogeneous logs without reliance on fixed templates. Experimental results validate the effectiveness of our approach on both binary and multi-class classification tasks. On the Monster-THC dataset, LogRESP-Agent achieved 99.97% accuracy and 97.00% F1-score, while also attaining 99.54% accuracy and 99.47% F1-score in multi-class classification using the EVTX-ATTACK-SAMPLES dataset. These results confirm the agent’s ability to not only detect complex threats but also explain them in context, offering a scalable foundation for next-generation threat detection and response automation.