Anomaly-based Intrusion Detection using Multiclass-SVM with Parameters Optimized by PSO
Guiping Wang, Shu Yu Chen, Jun Liu · International Journal of Security and Its Applications · 2015
Intrusion detection systems (IDS) play an important role in defending network systems from insider misuse as well as external attackers.Compared with misuse-based techniques, anomaly-based intrusion detection techniques perform well in detecting new attacks.Firstly, this paper proposes a feature selection algorithm based on SVM (termed FS-SVM) to reduce the dimensionality of sample data.Moreover, this paper presents an anomaly-based intrusion detection algorithm, i.e., multiclass support vector machine (MSVM) with parameters optimized by particle swarm optimization (PSO) (termed MSVM-PSO), to detect anomalous connections.To verify the effectiveness of these two proposed algorithms (FS-SVM and MSVM-PSO) and the detection precision of MSVM-PSO, this paper conducts experiments on the famous KDD Cup dataset.This paper compares MSVM-PSO with three commonly adopted algorithms, namely, Bayesian, K-Means, and multiclass SVM with parameters optimized grid method (MSVM-grid).The experimental results show that MSVM-PSO outperforms these three algorithms in detection accuracy, FP rate, and FN rate.