AI-Assisted Web Server Log Interpretation Using LLaMA Model
Abstract
Modern web infrastructures generate large volumes of access logs that contain valuable information related to system activity, user behaviour, performance monitoring, and cybersecurity threats. Manual analysis of these logs is time-consuming, error-prone, and inefficient for detecting suspicious activities in real time. This project presents the design and implementation of a Local AI System for Log Interpretation using LLaMA models, capable of performing automated log analysis, anomaly detection, AI-assisted summarization, and conversational log investigation within a privacy-preserving local execution environment. The system integrates Django-based operational management, Linux scheduling utilities (Cron and At), quantized GGUF(GPT-Generated Unified Format) LLaMA models, and Retrieval-Augmented Generation (RAG) techniques to support both scheduled analysis workflows and interactive “Chat with AI” operations. The log files uploaded/access from read-only specified location are passed to the AI inference engine, where log parsing, OWASP-based threat detection, contextual analysis, and structured JSON response generation are performed using locally deployed LLaMA models. Through CPU-compatible inference utilising llama-cpp-python, the project maintains lightweight deployment while enabling instantaneous, recurring, and scheduled analysis execution. GPU-accelerated environments such as Google Colab and Kaggle were additionally utilized for large-scale inference evaluation and high-volume log processing experiments. The conversational RAG workflow enables users to interact directly with uploaded logs using natural language queries and receive AI-generated cybersecurity insights, suspicious activity explanations, and remediation recommendations. Experimental evaluation demonstrates reliable task execution, efficient response generation, structured report handling, and effective contextual reasoning capabilities for both scheduled analysis and conversational AI-assisted workflows. The proposed system provides a modular, privacy-preserving, and locally deployable solution for intelligent cybersecurity log analysis and operational threat interpretation.