A Comparison of Support Vector Machine and Multi-Level Support Vector Machine on Intrusion Detection

Accessibility and openness of the Internet cause increase information security risk. Information security means protecting information from unallowed access, use, disruption, change and etc. This paper is about Intrusion Detection. The main goal of IDS (Intrusion Detection System) is to protect the system by analyzing users behaviors and habits when they are working with system, detect behaviors that don’t match with previously learned normal behaviors patterns and raise a warning. Support Vector Machine (SVM) is a classification method that used for IDS in many researches. We compare performance of SVM and Multi-Level Support Vector Machine (MLSVM) as a new edition of SVM on a challenging intrusion detection data set based on KDD’99 with name NSL-KDD. Our experiments indicate that MLSVM is more suitable for this data set rather than SVM.