Intrusion Detection and Prevention in Networks Using Web Data Mining Technology

The network information security problem has been a fundamental factor that limits internet application. Intrusion Detection is considered one of the network security fields of technology major scientific investigation directions. Web data mining technology will be implemented to NIDS (Network Intrusion Detection System), which may automatically detect the latest pattern from the huge network data, to decrease the workload of the manual compiling normal behavioral patterns and intrusion behavioural patterns. This study examined the recent intrusion detection technologies and the data mining techniques briefly. Emphasis on the web data mining algorithm in misuse detection and anomaly detection of particular applications. The classification algorithm is utilized for misuse detection. The clustering algorithm is utilized for anomaly detection. In pattern contrast to study deeply the sequence rules and association rules. Finally, the difficulties have been studied where the current data mining algorithm in intrusion detection applications encountered at present and has revealed the next research direction. In this article, the structure model, function modules, workflow, and functional features of the intrusion detection system which is based on web data mining technology.