Layered approach for intrusion detection using naïve Bayes classifier

Intrusion detection is the process of monitoring and analyzing the events occurring in a computer system in order to detect signs of security problems. In real world environment, the minority intrusion attacks namely R2L and U2R/Data attacks are more dangerous than the majority attacks like Probe and DoS. The present day standalone intrusion detection systems are not effective in detecting the minority attacks. Hence, it is essential to improve the detection performance for the minority intrusions, while maintaining a reasonable overall detection rate. In this paper we propose layered approach for improving the minority attack detection rate without hurting the prediction performance of the majority attacks. The proposed model used Naive Bayes classifier on reduced dataset for each attack class. In this system every layer is separately trained to detect a single type of attack category.