A Novel Intrusion Detection Approach using MultiKernel Functions
Abstract: Network intrusion
detection finds variant applications in computer and network industry. How toachieve
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 accurateintrusion
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 theoptimization 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.
Keywords: network intrusion
detection, multi-kernel function based kernel direct discriminant analysis, kernel
extreme learning machine, quantum particle swarm optimization
Author: Li Jiao Pan, Weijian
Jin, Jin Wu
Journal Code: jptkomputergg140134
