Research on Web Application Anomaly Intrusion Detection Model Based On Massive Logs

Abstract

Web log data in university application system is an important source of system operation and maintenance and security analysis. Based on MapReduce architecture, combined with the learning and detection model of attribute length, character distribution characteristics and attribute domain enumeration, this paper presents a massive data intrusion detection learning model and detection algorithm. The system operation results show that the platform can effectively find abnormal intrusion in the campus network, has high retrieval efficiency, and can effectively provide operation and maintenance efficiency and abnormal troubleshooting speed.