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 in- fluence on learning and prediction results for IDSs. How- ever, selecting feasible parameters can be time-consuming as the number of parameters and the size of the dataset in- crease. In this paper, an immune evolutionary based ker- nel 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 compar- ing the prediction performance using different types of ker- nels. At the same time, the parameter selection efficiency of the proposed approach is also compared with the differen- tial evolution algorithm.
['Chun Yang', 'Haidong Yang', 'Feiqi Deng']
A Parameter Selection Approach for Mixtures of Kernels Using Immune Evolutionary Algorithm and its Application to IDSs