LogEagle: A Framework for Web Server Log Analysis

Web server logs contain rich information on user activities and system events, makingtheir analysisvital for improving website performance, security, and user experience. This paper presents LogEagle,acomprehensive framework for web server log analysis that integrates real-time monitoring, anomalydetection, and interactive visualization. We detail methodologies for collecting rawlog data fromservers,preprocessing and structuring logs into analyzable formats, and extracting meaningful features. Advancedanalysis techniques, including statistical trend analysis and anomaly detection, are appliedtouncoverusage patterns and identify irregularities. The implementation leverages open-source tools (ElasticsearchLogstash-Kibana stack and custom Python modules) to achieve scalable, real-time logprocessing.Experimental results on real web server log data demonstrate the effectiveness of the systeminidentifyinguser behavior trends and detecting anomalies (such as unusual traffic spikes or error events) withlowlatency. The paper also discusses insights gained from visualizing log data and evaluates the framework’sperformance under various load conditions. We conclude by summarizing contributions andoutliningfuture enhancements, such as incorporating machine learning models for predictive analyticsandextending the system to.