Intrusion Detection System with Multi Layer using Bayesian Networks

Abstract

In the era of network security, intrusion detection system plays a vital to detect real-time intrusions, and to execute work to stop the attack. Being everything shifting to internet, security became the foremost preference. In real world, the minority attacks R2L (Remote-To-User) and U2R (User-To-Root) are more hazardous than Probe and DoS (Denial-Of-Service) majority attacks. Present IDS are not much efficient to detect these low level attacks. Therefore, it is extremely important to improve the detection performance for the R2L and U2R attacks with the majority attacks. In this paper hierarchical layered approach for improving detection rate of minority attacks as well as majority attacks is propound. The propound model used Naive bayes classifier with K2 learning process on reduced NSL KDD dataset for each attack class. In this method every layer is individually trained to detect a single type of attack category and the outcome of one layer is passed into another layer to increase the detection rate and for better categorization of both the majority and minority attacks.