• DocumentCode
    2023048
  • Title

    An overview of neural networks use in anomaly Intrusion Detection Systems

  • Author

    Sani, Yusuf ; Mohamedou, Ahmed ; Ali, Khalid ; Farjamfar, Anahita ; Azman, Mohamed ; Shamsuddin, Solahuddin

  • Author_Institution
    Dept. of Commun. Technol. & Networks, Univ. Putra Malaysia, Serdang, Malaysia
  • fYear
    2009
  • fDate
    16-18 Nov. 2009
  • Firstpage
    89
  • Lastpage
    92
  • Abstract
    With the increasing number of computers being connected to the Internet, security of an information system has never been more urgent. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. This is the reason of an entire area of research, called Intrusion Detection Systems (IDS). Anomaly systems detect intrusions by searching for an abnormal system activity. But the main problem of anomaly detection IDS is that; it is very difficult to build, because of the difficulty in defining what is normal and what is abnormal. Neural network with its ability of learning has become one of the most promising techniques to solve this problem. This paper presents an overview of neural networks and their use in building anomaly intrusion systems.
  • Keywords
    learning (artificial intelligence); neural nets; security of data; abnormal system activity search; anomaly systems; intrusion detection systems; learning ability; neural networks; Anomaly Detection; Intrusion Detection Systems; Neural Network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Research and Development (SCOReD), 2009 IEEE Student Conference on
  • Conference_Location
    Serdang
  • Print_ISBN
    978-1-4244-5186-9
  • Electronic_ISBN
    978-1-4244-5187-6
  • Type

    conf

  • DOI
    10.1109/SCORED.2009.5443289
  • Filename
    5443289