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http://dx.doi.org/10.3745/KIPSTC.2004.11C.5.595

Anomaly Intrusion Detection using Fuzzy Membership Function and Neural Networks  

Cha, Byung-Rae (여수대학교 전산학과)
Publication Information
The KIPS Transactions:PartC / v.11C, no.5, 2004 , pp. 595-604 More about this Journal
Abstract
By the help of expansion of computer network and rapid growth of Internet, the information infrastructure is now able to provide a wide range of services. Especially open architecture - the inherent nature of Internet - has not only got in the way of offering QoS service, managing networks, but also made the users vulnerable to both the threat of backing and the issue of information leak. Thus, people recognized the importance of both taking active, prompt and real-time action against intrusion threat, and at the same time, analyzing the similar patterns of in-trusion already known. There are now many researches underway on Intrusion Detection System(IDS). The paper carries research on the in-trusion detection system which hired supervised learning algorithm and Fuzzy membership function especially with Neuro-Fuzzy model in order to improve its performance. It modifies tansigmoid transfer function of Neural Networks into fuzzy membership function, so that it can reduce the uncertainty of anomaly intrusion detection. Finally, the fuzzy logic suggested here has been applied to a network-based anomaly intrusion detection system, tested against intrusion data offered by DARPA 2000 Intrusion Data Sets, and proven that it overcomes the shortcomings that Anomaly Intrusion Detection usually has.
Keywords
Anomaly Intrusion Detection; Fuzzy Membership Function; Neural Network;
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