원문정보
초록
영어
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.
목차
1. Introduction
2. Related Work
2.1. Intrusion Detection
2.2. Feature Selection (FS)
2.3. Support Vector Machines (SVMs)
3. Preliminaries
3.1. The Basic Idea of SVM
3.2. C-support Vector Classification (C-SVC)
4. A Feature Selection Algorithm based on SVM (FS-SVM)
5. An Anomaly-Based Intrusion Detection Algorithm based on Multiclass SVM with Parameters Optimized by PSO
5.1. A multi-Class Classification Algorithm based on SVM
5.2. Parameter Optimization by PSO
6. Experiments and Analyses
6.1. Dataset Description
6.2. The Selected Features
6.3. Experimental Results and Analyses
7. Conclusion and Future Work
Acknowledgments
References