Analysis and Evaluation of Hybrid Intrusion Detection System Models

Detecting intrusions is becoming an important and yet challenging task in information systems. This led to an increasing need for efficient methods of recognising intrusions in order to protect the systems. Existing models of intrusion detection systems (IDS) have produced significant performance but often possesses the inability for detecting multilevel classes of attacks coupled with high training time for classifiers. These drawbacks led to hybrid models that combine the various strengths of single classifiers at the same time avoiding their weaknesses for better performance. In this paper, a comparison of such hybrid models is carried out. The objective is to determine their performances and isolate their weaknesses. Thus, a research gap is established for more efficient intrusion detection models.