期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2015
卷号：9
期号：6
页码：227-242
DOI：10.14257/ijsia.2015.9.6.22
出版社：SERSC
摘要：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.
