Anomaly Intrusion Detection Based on A hybrid GSA-SVM Classifier

Abstract

Support vector machine (SVM)  is commonly used in IDSs because of its robustness and efficiency in the network classification. However, Parameter optimization influences the classification accuracy of SVM significantly. In order to improve, a hybrid classifier is designed based on a combination of the GSA and SVM algorithms to optimize the accuracy of the SVM classifier. In the GSASVM classifier, the GSA guides the selection of potential subsets that lead to the best detection accuracy. The GSA-SVM algorithm evaluated using KDD CUP 99 data set and compared to the outperformance of the original SVM algorithms. The results show that the performance of GSA-SVM algorithm has higher detection rate with lower false positive rate.

Keywords: Network Intrusion Detection, Gravitational Search Algorithm (GSA), support vector machines (SVM)