Application of intelligent information processing technologies to intrusion detection system

In Intrusion Detection Systems, attack-specific signatures are used to detect known attacks in network traffic. Finding anomalous network activity is difficult and tedious. Many intelligent information processing methods, data miming technology and so on have been applied to generating attack signatures automatically, updating signatures easily and improving detection accuracy with ultra data set. An application is built in which we employ rough set theory, association rule discovery and naive Bayesian learning to automatically generate rules for classifying network connections. This paper describes the methodology and the applications employing this methodology. Empirical results illustrate that the intrusion detection model can detect intrusion accurately.