Volume 9 Number 5 (May 2014)
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JCP 2014 Vol.9(5): 1117-1124 ISSN: 1796-203X
doi: 10.4304/jcp.9.5.1117-1124

GPU Implementation of Parallel Support Vector Machine Algorithm with Applications to Intruder Detection

Xueqin Zhang1, Yifeng Zhang1, Chunhua Gu2
1East China University of Science and Technology, Shanghai, China
2Shanghai University of Electric Power, Shanghai, China


Abstract—The network anomaly detection technology based on support vector machine (SVM) can efficiently detect unknown attacks or variants of known attacks, however, it cannot be used for detection of large-scale intrusion scenarios due to the demand of computational time. The graphics processing unit (GPU) has the characteristics of multi-threads and powerful parallel processing capability. Based on the system structure and parallel computation framework of GPU, a parallel algorithm of SVM, named GSVM, is proposed. Extensive experiments were carried out onKDD99 and other large-scale datasets, the results showed that GSVM significantly improves the efficiency of intrusion detection, while retaining detection performance.

Index Terms—Network Intrusion Detection, Support Vector Machine, GPU, Parallel Algorithm

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Cite: Xueqin Zhang, Yifeng Zhang, Chunhua Gu, "GPU Implementation of Parallel Support Vector Machine Algorithm with Applications to Intruder Detection," Journal of Computers vol. 9, no. 5, pp. 1117-1124, 2014.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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