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Improved Detection Approach for Distributed Denial of Service Attack Based on SVM

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3 Author(s)
Xiang Xu ; Sch. of Mech. & Mater., China Three Gorges Univ., Yichang, China ; Ding Wei ; Yuelei Zhang

The intrusion detection rate is greatly influenced by the parameters of the support vector machine (SVM) model. In order to overcome the parameter limits to improve the identify accuracy of Distributed Denial of Service (DDoS) attack, this paper presents a new detection method based on Kernel Principle Component Analysis (KPCA) and Particle Swarm Optimization (PSO)-Support Vector Machine (SVM). The KPCA was used to obtain the important characteristics of the intrusion data to eliminate the redundant features. Then the PSO was used to optimize the SVM parameters. Experimental results show the proposed approach can enhance the detection rate, and performs better than the PCA based methods.

Published in:

Circuits, Communications and System (PACCS), 2011 Third Pacific-Asia Conference on

Date of Conference:

17-18 July 2011

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