A Framework for an Adaptive Anomaly Detection System with Fuzzy Data Mining

In this paper, we present an adaptive anomaly detection framework that isapplicable to network-based intrusion detection. Our framework employs fuzzy cluster algorithm to detect anomalies in an online, adaptive fashion without a priori knowledge of the underlying data. We evaluate our method by perf...

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Bibliographic Details
Published inWuhan University journal of natural sciences Vol. 11; no. 6; pp. 1797 - 1800
Main Authors Xiang, Gao, Min, Wang, Rongchun, Zhao
Format Journal Article
LanguageEnglish
Published School of Computer, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China 01.11.2006
Subjects
侵入检测
异常检测
数据挖掘
模糊集
网络安全
intrusion detection
anomaly detection
network security
unsupervised
fuzzy cluster
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Summary:In this paper, we present an adaptive anomaly detection framework that isapplicable to network-based intrusion detection. Our framework employs fuzzy cluster algorithm to detect anomalies in an online, adaptive fashion without a priori knowledge of the underlying data. We evaluate our method by performing experiments over network records from the KDD CUP99 data set.
Bibliography:intrusion detection; anomaly detection; fuzzy cluster; unsupervised; network security
TP393.08
network security
unsupervised
intrusion detection
anomaly detection
42-1405/N
fuzzy cluster
ISSN:1007-1202
1993-4998
DOI:10.1007/BF02831878