Research Article
Wavelet-based Real Time Detection of Network Traffic Anomalies
@INPROCEEDINGS{10.1109/SECCOMW.2006.359584, author={Chin-Tser Huang and Sachin Thareja and Yong-June Shin}, title={Wavelet-based Real Time Detection of Network Traffic Anomalies}, proceedings={1st International ICST Workshop on Enterprise Network Security}, publisher={IEEE}, proceedings_a={WENS}, year={2007}, month={5}, keywords={network traffic anomaly; intrusion detection; wavelet; percentage deviation; entropy}, doi={10.1109/SECCOMW.2006.359584} }- Chin-Tser Huang
Sachin Thareja
Yong-June Shin
Year: 2007
Wavelet-based Real Time Detection of Network Traffic Anomalies
WENS
IEEE
DOI: 10.1109/SECCOMW.2006.359584
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