Soft Computing Models for Network Intrusion Detection Systems
<|reference_start|>Soft Computing Models for Network Intrusion Detection Systems: Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: external intruders, who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. This chapter presents a soft computing approach to detect intrusions in a network. Among the several soft computing paradigms, we investigated fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming and an ensemble method to model fast and efficient intrusion detection systems. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.<|reference_end|>
@article{abraham2004soft,
    title={Soft Computing Models for Network Intrusion Detection Systems},
    author={Ajith Abraham and Ravi Jain},
    journal={Soft Computing in Knowledge Discovery: Methods and Applications,
  Saman Halgamuge and Lipo Wang (Eds.), Studies in Fuzziness and Soft
  Computing, Springer Verlag Germany, Chapter 16, 20 pages, 2004},
    year={2004},
    archivePrefix={arXiv},
    eprint={cs/0405046},
    primaryClass={cs.CR}
}