Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs

• URL: http://arxiv.org/abs/2510.00529v1
• Date: Wed, 01 Oct 2025 05:23:05 GMT
• Title: Memory-Augmented Log Analysis with Phi-4-mini: Enhancing Threat Detection in Structured Security Logs
• Abstract summary: We propose textbfDM-RAG, a dual-memory retrieval-augmented generation framework for structured log analysis.<n>It integrates a short-term memory buffer for recent summaries and a long-term FAISS-indexed memory for historical patterns.<n>On the UNSW-NB15 dataset, DM-RAG 53.64% accuracy and 98.70% recall, surpassing fine-tuned and RAG baselines in recall.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structured security logs are critical for detecting advanced persistent threats (APTs). Large language models (LLMs) struggle in this domain due to limited context and domain mismatch. We propose \textbf{DM-RAG}, a dual-memory retrieval-augmented generation framework for structured log analysis. It integrates a short-term memory buffer for recent summaries and a long-term FAISS-indexed memory for historical patterns. An instruction-tuned Phi-4-mini processes the combined context and outputs structured predictions. Bayesian fusion promotes reliable persistence into memory. On the UNSW-NB15 dataset, DM-RAG achieves 53.64% accuracy and 98.70% recall, surpassing fine-tuned and RAG baselines in recall. The architecture is lightweight, interpretable, and scalable, enabling real-time threat monitoring without extra corpora or heavy tuning.

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