• DocumentCode
    1634174
  • Title

    An intrusion detection method based on rough set and SVM algorithm

  • Author

    Hong, Peng ; Zhang, Dongna ; Wu, Tiefeng

  • Author_Institution
    Sch. of Comput. & Math.-Phys. Sci., Xihua Univ., Chengdu, China
  • Volume
    2
  • fYear
    2004
  • Firstpage
    1127
  • Abstract
    This paper proposes an intrusion detection method that combines rough sets and SVM algorithm. By virtue of the ability rough sets have to decrease the amount of data and get rid of redundancy, the method can reduce the amount of training data and overcome the SVM defect of slow running speed when processing large datasets. At the same time, by the aid of the SVM algorithm the method can classify the core of the property set to have extensiveness and high identification rate, and avoid disturbances. Experimental results show this method is better than other methods reported in the literature in terms of detection resolution.
  • Keywords
    authorisation; computer network management; data reduction; rough set theory; support vector machines; SVM algorithm; data reduction; identification rate; intrusion detection method; rough sets; Computer networks; Computer security; Data security; Intrusion detection; Learning systems; Machine learning algorithms; Redundancy; Support vector machine classification; Support vector machines; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications, Circuits and Systems, 2004. ICCCAS 2004. 2004 International Conference on
  • Print_ISBN
    0-7803-8647-7
  • Type

    conf

  • DOI
    10.1109/ICCCAS.2004.1346374
  • Filename
    1346374