• DocumentCode
    2494711
  • Title

    Research Intrusion Detection Techniques from the Perspective of Machine Learning

  • Author

    Hui, Liu ; Cao Yonghui

  • Author_Institution
    Sch. of Comput. & Inf. Technol., Henan Normal Univ., Xinxiang, China
  • Volume
    1
  • fYear
    2010
  • fDate
    24-25 April 2010
  • Firstpage
    166
  • Lastpage
    168
  • Abstract
    With the rapid development of the Internet services and the fast increasing of intrusion problems, the traditional intrusion detection methods cannot work well with the more and more complicated intrusions. So introducing machine learning into intrusion detection systems to improve the performance has become one of the major concerns in the research of intrusion detection. Intrusion detection systems were proposed to complement prevention-based security measures. In this paper, we first introduces the basic structure of the intrusion detection system, then analysis intrusion Detection Techniques Based on Machine Learning Method, including the Bayesian based method, the neural network based method, the data mining based method and the SVM based method.
  • Keywords
    Bayes methods; Internet; data mining; learning (artificial intelligence); neural nets; security of data; support vector machines; Bayesian based method; Internet services; SVM based method; data mining based method; intrusion detection techniques; machine learning; neural network based method; prevention based security measures; Bayesian methods; Data mining; Data security; Information technology; Intrusion detection; Learning systems; Machine learning; Neural networks; Support vector machines; Web and internet services;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Information Technology (MMIT), 2010 Second International Conference on
  • Conference_Location
    Kaifeng
  • Print_ISBN
    978-0-7695-4008-5
  • Electronic_ISBN
    978-1-4244-6602-3
  • Type

    conf

  • DOI
    10.1109/MMIT.2010.161
  • Filename
    5474252