Large Language Models For Analyzing E-Commerce Web Server Logs

The rapid advancement of Large Language Models (LLMs) has opened new possibilities for analyzing Web server access logs, particularly in the e-commerce domain. Web server logs provide a rich source of data for system monitoring, security assessment, and user behavior analysis. LLMs may facilitate automated log parsing, anomaly detection, and user session analysis, reducing manual effort, typically required for this kind of tasks. The paper investigates the applicability of a popular LLM, ChatGPT-4o, for Web server log data analysis. A series of experiments on a real e-commerce server log was performed, focusing on six key areas: general statistics and automated reporting, user session reconstruction and analysis, error diagnosis and anomaly detection, Web bot detection, generating synthetic user sessions, and analysis of e-customer activities. The results demonstrate the efficiency of LLMs in extracting meaningful insights from raw log data without extensive pre-processing, making advanced log analysis more accessible to researchers and practitioners. However, challenges such as necessity of precise prompt formulation, the risk of LLM-generated hallucinations, and generalized interpretations require careful validation. The study provides practical guidelines for integrating LLMs into e-commerce server log analysis, emphasizing their advantages, limitations, and potential business applications.