Network Intrusion Detection Using Wavelet Analysis

The inherent presence of self-similarity in network (LAN, Internet) traffic motivates the applicability of wavelets in the study of ‘burstiness’ features of them. Inspired by the methods that use the self-similarity property of a data network traffic as normal behaviour and any deviation from it as...

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Bibliographic Details
Published inIntelligent Information Technology pp. 224 - 232
Main Authors Rawat, Sanjay, Sastry, Challa S.
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 01.01.2004
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Burstiness
Computer science; control theory; systems
Energy plot
Exact sciences and technology
Hurst parameter
Intrusion detection
Learning and adaptive systems
Network traffic
Self-similarity
Wavelets
Similarity
Wavelet transformation
Anomaly
Intruder detector
Internet
Localization
Artificial intelligence
Computer security
Local network
Intrusion detection systems
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Summary:The inherent presence of self-similarity in network (LAN, Internet) traffic motivates the applicability of wavelets in the study of ‘burstiness’ features of them. Inspired by the methods that use the self-similarity property of a data network traffic as normal behaviour and any deviation from it as the anomalous behaviour, we propose a method for anomaly based network intrusion detection. Making use of the relations present among the wavelet coefficients of a self-similar function in a different way, our method determines the possible presence of not only an anomaly, but also its location in the data. We provide the empirical results on KDD data set to justify our approach.
ISBN:9783540241263
3540241264
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-30561-3_24