Intrusion Detection Using Combination of Various Kernels Based Support Vector Machine
Mohammed Nasser, Shamim Ahmad · 2013
The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with many features. To get rid of this problem, several types of intrusion detection methods have been proposed and shown different levels of accuracy. This is why, the choice of the effective and robust method for IDS is very important topic in information security. In this paper, a combining classification approach to network intrusion detection based on the fusion of multiple classifiers is proposed. This approach makes a combination of various kernel based Support Vector Machine (SVM) classifier for intrusion detection system using majority voting fusion strategy. The experimental results indicate that combined approach effectively generates a more accurate model compared to single kernel based SVM classifier for the problem of intrusion detection.