A real time IDSs based on artificial Bee Colony-support vector machine algorithm

Abstract

The success of any Intrusion Detection Systems (IDSs) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with irrelevant and redundant features. How to choose the effective and key features is very important topic for an intrusion detection problem. Support vector machine (SVM) has been employed to provide potential solutions for the IDSs problem. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Artificial Bee Colony algorithm (ABC) is an optimization method, which is not only has strong global search capability, but also is very easy to implement. Thus, the proposed ABC-SVM model is applied to determine free parameters of SVM and feature selection at building intrusion detection system. The standard ABC is used to determine free parameters of support vector machine and the binary ABC is to obtain the optimum feature selection for IDSs from KDD Cup 99 data set. The experimental results indicate that the ABC-SVM method can achieve higher accuracy rate than Particle swarm optimization (PSO) and GA-SVM algorithms in the same time.