GPU Implementation of Parallel Support Vector Machine Algorithm with Applications to Detection Intruder
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}
}
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.
May 9, 2014 by hgpu
