12027

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

Xueqin Zhang, Yifeng Zhang, Chunhua Gu
East China University of Science and Technology, Shanghai, China
Journal of Computers, Vol. 9, No. 5, 2014

@article{zhang2014gpu,

   title={GPU Implementation of Parallel Support Vector Machine Algorithm with Applications to Detection Intruder},

   author={Zhang, Xueqin and Zhang, Yifeng and Gu, Chunhua},

   journal={Journal of Computers},

   volume={9},

   number={5},

   pages={1117–1124},

   year={2014}

}

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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.
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