Layered Intrusion Detection System Model for The Attack Detection with The Multi-Class Ensemble Classifier

Abstract

This paper presented a layered Intrusion Detection System (IDS) for attack detection in the network. The developed model comprises of the Multi-Class Hybrid Ensemble Learning in the IDS system termed the MCEL-IDS. The proposed MCEL-IDS perform ensemble learning for attack detection in the network. The MCEL-IDS system comprises multi-class features for the consideration of the attributes in the network. The experimental analysis expressed that the MCEL-IDS model achieves a higher False Positive Rate compared with the existing classifier. The MCEL-IDS achieves a higher FPR value of 0.86 which is ~12% performance improvement than the existing classifier.