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IJNSEC
2008

Wavelet-based Real Time Detection of Network Traffic Anomalies

9 years 11 months ago
Wavelet-based Real Time Detection of Network Traffic Anomalies
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. Keywords-network traffic anomaly; intrusion detection; wavelet; percentage deviation; entropy
Chin-Tser Huang, Sachin Thareja, Yong-June Shin
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where IJNSEC
Authors Chin-Tser Huang, Sachin Thareja, Yong-June Shin
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