Wavelet-based real time detection of network traffic anomalies

Chin Tser Huang, Sachin Thareja, Yong June Shin

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Real time network monitoring for intrusions is offered by various host and network based intrusion detection systems. These systems largely use signature or pattern matching techniques at the core and thus are ineffective in detecting unknown anomalous activities. In this paper, we apply signal processing techniques in intrusion detection systems, and develop and implement a framework, called Waveman, for real time wavelet-based analysis of network traffic anomalies. Then, we use two metrics, namely percentage deviation and entropy, to evaluate the performance of various wavelet functions on detecting different types of anomalies like Denial of Service (DoS) attacks and portscans. Our evaluation results show that Coiflet and Paul wavelets perform better than other wavelets in detecting most anomalies considered in this work.

Original languageEnglish
Pages (from-to)309-320
Number of pages12
JournalInternational Journal of Network Security
Volume6
Issue number3
Publication statusPublished - 2008

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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