A SVM intrusion detection method based on GPU

Yongxiang Xia, Zhicai Shi, Yu Zhang, Jian Dai

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

To optimize training procedure of IDS based on SVM and reduce time consumption, a SVM intrusion detection method based on GPU is proposed in the study. During the simulation experiments with KDD Cup 1999 data, GPU-based parallel computing model is adopted. Results of the simulation experiments demonstrate that time consumption in the training procedure of IDS is reduced, and performance of IDS is kept as usual.
Original languageEnglish
Title of host publicationMechanics, mechatronics, intelligent system and information technology
Subtitle of host publicationselected, peer reviewed papers from the 2014 International Conference on Applied Mechanics, Mechatronics and Intelligent System (AAMIS 2014), April 18-20, 2014, Changsha, China
EditorsJun Wang
Place of PublicationSwitzerland
PublisherTrans Tech Publications
Pages606-610
Number of pages5
ISBN (Print)9783038351757
DOIs
Publication statusPublished - 2014
EventInternational Conference on Applied Mechanics, Mechatronics and Intelligent System - Changsha
Duration: 18 Apr 201420 Apr 2014

Publication series

NameApplied Mechanics and Materials
PublisherTrans Tech Publications
Volume610
ISSN (Print)1660-9336

Conference

ConferenceInternational Conference on Applied Mechanics, Mechatronics and Intelligent System
CityChangsha
Period18/04/1420/04/14

Keywords

  • CUDA
  • GPU
  • Intrusion detection
  • SVM

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  • Cite this

    Xia, Y., Shi, Z., Zhang, Y., & Dai, J. (2014). A SVM intrusion detection method based on GPU. In J. Wang (Ed.), Mechanics, mechatronics, intelligent system and information technology: selected, peer reviewed papers from the 2014 International Conference on Applied Mechanics, Mechatronics and Intelligent System (AAMIS 2014), April 18-20, 2014, Changsha, China (pp. 606-610). (Applied Mechanics and Materials; Vol. 610). Switzerland: Trans Tech Publications. https://doi.org/10.4028/www.scientific.net/AMM.610.606