Hybrid Intrusion Detection Systems (HIDS) using Fuzzy Logic

Part of book or chapter of book English OPEN
Shanmugam, Bharanidharan; Idris, Norbik Bashah;
(2011)
  • Publisher: InTech
  • References (48)
    48 references, page 1 of 5

    Aickelin, U., P. Bentley, et al. (2003). Danger theory: The link between ais and ids. Proceedings of Second International Conference on Artificial Immune Systems (ICARIS-03),147-155.

    Aly El-Semary, Janica Edmonds, et al. (2006). Applying Data Mining of Fuzzy Association rules to Network Intrusion Detection. Proceedings of IEEE workshop on Information Assurance,100-107.

    Axelsson, S. (1998). Research in intrusion-detection systems: a survey. Goteborg, Sweden,, Department of Computer Engineering, Chalmers University of Technology.

    Barbará, D., J. Couto, et al. (2001). ADAM: a testbed for exploring the use of data mining in intrusion detection. ACM SIGMOD 30(Special):pp. 15-24

    Breiman, L. (2001). Random forests. Machine Learning 45(-):pp. 5-32

    Bridges, S. M. and R. B. Vaughn (2000). Fuzzy data mining and genetic algorithms applied to intrusion detection. Proceedings of National Information Systems Security Conference Baltimore.

    Cannady, J. (1999). Artificial Neural Networks for Misuse Detection. Proceedings of National Information Systems Security Conference,443-456, Arlington.

    Cansian A, M., Moreira E, et al. (1997). Network intrusion detection using neural networks. Proceedings of International conference on computational intelligence and multimedia applications ICCMA.

    Debar, H., M. Dacier, et al. (1998). A workbech for Intrusion detection systems. IBM Zurich Research Laboratory:pp.

    Diaz-Gomez. and D. F. Hougen (2005). Analysis and mathematical justification of a fitness function used in an intrusion detection system. Proceedings of Genetic and Evolutionary Computation Conference,1591-1592, ACM.

  • Metrics
Share - Bookmark

  • Download from
    InTech via InTech (Part of book or chapter of book, 2011)
  • Cite this publication