D-SCIDS: Distributed soft computing intrusion detection system

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@Article{Abraham:2007:JNCS,

• 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 detection.",

}

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

Citations