文献类型：期刊文献

英文题名：GPU Implementation of Parallel Support Vector Machine Algorithm with Applications to Intruder Detection

作者：Zhang, Xueqin[1];Zhang, Yifeng[2];Gu, Chunhua[3]

机构：[1]East China Univ Sci & Technol, Detect Technol & Automat Devices, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Elect & Commun, Shanghai, Peoples R China;[3]Shanghai Univ Elect Power, Shanghai, Peoples R China

年份：2014

卷号：9

期号：5

起止页码：1117

外文期刊名：JOURNAL OF COMPUTERS

收录：WOS:【ESCI(收录号:WOS:000218207100012)】；

语种：英文

外文关键词：Network Intrusion Detection; Support Vector Machine; GPU; Parallel Algorithm

摘要：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.