Performance Evaluation of PSVM Using Various Combination of Kernel Function for Intrusion Detection System

Abstract

The internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continuously, developing flexible and adaptive security oriented approaches is a severe challenge. In this context, intrusion detection technique is a valuable technology to protect target systems and networks against malicious activities. But this system doesn't provide the required accuracy. Thus to meet this requirement, this paper proposes an intrusion detection system as a model based on Proximal Support Vector Machines (PSVMs) implemented with various combination of basic kernel functions. PSVM is a light and simple modification of support vector machine. We have implemented PSVM for binary classification of intrusion detection data. For experimental training and testing NSL- KDD dataset is preprocessed using Principle Component Analysis technique. Using proposed classification model, we have achieved up to 79% classification accuracy.