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Weijian Jin
Yiwu Industrial & Commercial College
Computer Science & IT

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A Novel Intrusion Detection Approach using Multi-Kernel Functions Lijiao Pan; Weijian Jin; Jun Wu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.889

Abstract

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.
Co-Authors Jun Wu Lijiao Pan