Fuzzy Vector Quantization for Network Intrusion Detection

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10 Citations (Scopus)
26 Downloads (Pure)

Abstract

This paper considers anomaly network traffic detection using different network feature subsets. Fuzzy c-means vector quantization is used to train network attack models and the minimum distortion rule is applied to detect network attacks. We also demonstrate the effectiveness and ineffectiveness in finding anomalies by looking at the network data alone. Experiments performed on the KDD CUP 1999 dataset show that time based traffic features in the last two second time window should be selected to obtain highest detection rates
Original languageEnglish
Title of host publicationProceedings: 2007 IEEE International Conference on Granular Computing (GrC 2007)
EditorsT.Y Lin, X Hu
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages566-570
Number of pages5
ISBN (Print)9780769530321
DOIs
Publication statusPublished - 2007
EventIEEE International Conference on Granular Computing - San Jose, United States
Duration: 2 Nov 20074 Nov 2007

Conference

ConferenceIEEE International Conference on Granular Computing
Country/TerritoryUnited States
CitySan Jose
Period2/11/074/11/07

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