Wavelet-based real time detection of network traffic anomalies

Chin Tser Huang, Sachin Thareja, Yong June Shin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Citations (Scopus)


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
Title of host publication2006 Securecomm and Workshops
Publication statusPublished - 2006
Event2006 Securecomm and Workshops - Baltimore, MD, United States
Duration: 2006 Aug 282006 Sept 1

Publication series

Name2006 Securecomm and Workshops


Other2006 Securecomm and Workshops
Country/TerritoryUnited States
CityBaltimore, MD

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Communication


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