A Novel Intrusion Detection Approach using Multi-Kernel Functions
Network intrusion detection finds variant applications in computer and network industry. How to achieve high intrusion detection accuracy and speed is still received considerable attentions in this field. To address this issue, this work presents a novel method that takes advantages of multi-kernel computation technique to realize speedy and precise network intrusion detection and isolation. In this new development the multi-kernel function based kernel direct discriminant analysis (MKDDA) and quantum particle swarm optimization (QPSO) optimized kernel extreme learning machine (KELM) were appropriately integrated and thus form a novel method with strong intrusion detection ability. The MKDDA herein was firstly employed to extract distinct features by projecting the original high dimensionality of the intrusion features into a low dimensionality space. A few distinct and efficient features were then selected out from the low dimensionality space. Secondly, the KELM was proposed to provide quick and accurate intrusion recognition on the extracted features. The only parameter need be determined in KELM is the neuron number of hidden layer. Literature review indicates that very limited work has addressed the optimization of this parameter. Hence, the QPSO was used for the first time to optimize the KELM parameter in this paper. Lastly, experiments have been implemented to verify the performance of the proposed method. The test results indicate that the proposed LLE-PSO-KELM method outperforms its rivals in terms of both recognition accuracy and speed. Thus, the proposed intrusion detection method has great practical importance.
Zhu S, Hu B. Hybrid feature selection based on improved GA for the intrusion detection system. TELKOMNIKA. 2012; 11(4): 1725–1730.
Hou G, Ma X, Zhang Y. A new method for intrusion detection using manifold learning algorithm. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2013; 11(12): 7344–7350.
Zhu S, Hu B. Hybrid Feature Selection Based on Improved GA for the Intrusion Detection System. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2013; 11(4): 1725–1730.
Li F, Tang W, Duan H, Hao J. Application of fractional power polynomial kernel function to kernel direct discriminant analysis. Optics and Precision Engineering. 2007; 15(9): 1140-1144.
Li F, Xu K. Kernel model applied in kernel direct discriminant analysis for the recognition of face with nonlinear variations. Transactions of Tianjin University. 2006; 12(2): 147-152.
Liang L. Network intrusion detection system based on optimized Fuzzy rules algorithm. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2014; 12(4): 2816–2825.
Chen S, Diao H. A network intrusion detection method based on improved ACBM. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2014; 12(4): 2808–2815.
Liu L, Wan P, Wang Y, Liu S. Clustering and hybrid genetic algorithm based intrusion detection strategy. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2014; 12(1): 762–770.
Yu G, Weng K. Intrusion detection system and technology of layered wireless sensor network based on Agent. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2013; 11(8): 4238–4343.
Huang G, Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing. 2008; 71: 16–18.
Zong W, Huang G, Lin Z. Face recognition based on extreme learning machine. Neurocomputing. 2011; 74: 2541–2551.
Jiang Y, Wu J, Zong C. An effective diagnosis method for single and multiple defects detection in gearbox based on nonlinear feature selection and kernel-based extreme learning machine. Journal of Vibroengineering. 2014; 16(1): 499–512.
Sun H, Qi Y. A chaos cloud particle swarm algorithm based available transfer capability. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2014; 12(1): 38–47.
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