D-SCIDS: Distributed soft computing intrusion detection system

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Ajith Abraham and Ravi Jain and Johnson Thomas and 
                 Sang Yong Han",
  title =        "D-SCIDS: Distributed soft computing intrusion
                 detection system",
  journal =      "Journal of Network and Computer Applications",
  year =         "2007",
  volume =       "30",
  number =       "1",
  pages =        "81--98",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1016/j.jnca.2005.06.001",
  abstract =     "An Intrusion Detection System (IDS) is a program that
                 analyses what happens or has happened during an
                 execution and tries to find indications that the
                 computer has been misused. A Distributed IDS (DIDS)
                 consists of several IDS over a large network (s), all
                 of which communicate with each other, or with a central
                 server that facilitates advanced network monitoring. In
                 a distributed environment, DIDS are implemented using
                 co-operative intelligent agents distributed across the
                 network(s). This paper evaluates three fuzzy rule-based
                 classifiers to detect intrusions in a network. Results
                 are then compared with other machine learning
                 techniques like decision trees, support vector machines
                 and linear genetic programming. Further, we modelled
                 Distributed Soft Computing-based IDS (D-SCIDS) as a
                 combination of different classifiers to model
                 lightweight and more accurate (heavy weight) IDS.
                 Empirical results clearly show that soft computing
                 approach could play a major role for intrusion

Genetic Programming entries for Ajith Abraham Ravi Jain Johnson P Thomas Sang Yong Han