Research and Implementation of Campus Network Intrusion Detection System based on Data Mining and Image Processing

Abstract

In order to solve the problem of traditional intrusion detection system programs usually being manually written, with a large workload and certain limitations, the author proposes the research and implementation of a campus network intrusion detection system based on data mining and image processing. Its hardware components include a data warehouse, sensors, checkers, generators, etc. The software design includes a packet capture module, a data preprocessing module, and an event analyzer module. Experimental comparison with traditional methods. The experimental results show that the campus network intrusion detection system designed by the author is far superior to traditional system design in detecting intrusion behavior, approaching 100% infinitely, and has high effectiveness. The system has a certain degree of adaptive ability and can effectively detect external intrusions.