A Parameter Selection Approach for Mixtures of Kernels using Immune Evolutionary Algorithm and Its Application to IDSs

摘要

Supervised anomaly intrusion detection systems (IDSs) based on Support Vector Machines (SVMs) classification technique have attracted much more attention today.In these systems,the characteristics of kernels have great influence on learning and prediction results for IDSs.However,selecting feasible parameters can be time-consuming as the number of parameters and the size of the dataset increase.In this paper,an immune evolutionary based kernel parameter selection approach is proposed.Through the simulation of the denial of service attacks in mobile ad-hoc networks (MANETs),the result dataset is used for comparing the prediction performance using different types of kernels.At the same time,the parameter selection efficiency of the proposed approach is also compared with the differential evolution algorithm.