An AI-Based Risk Analysis Framework Using Large Language Models for Web Log Security

Abstract

Web log data analysis is essential for monitoring and securing modern software systems. However, traditional manual analysis methods struggle to cope with the rapidly growing volumes and complexity of log data, resulting in inefficiencies and potential security risks. To address these challenges, this paper proposes an AI-driven log analysis framework utilizing advanced natural language processing techniques from large language models (LLMs), specifically ChatGPT. The framework aims to automate log data normalization, anomaly detection, and risk assessment, enabling the real-time identification and mitigation of security threats. Our objectives include reducing dependency on human analysis, enhancing the accuracy and speed of threat detection, and providing a scalable solution suitable for diverse web service environments. Through extensive experimentation with realistic log scenarios, we demonstrate the effectiveness of the proposed framework in swiftly identifying and responding to web-based security threats, ultimately improving both security posture and operational efficiency.