A real-time and reliable approach to detecting traffic variations at abnormally high and low rates

Authors:
Ming Li;Shengquan Wang;Wei Zhao
Affiliations:
School of Information Science & Technology, East China Normal University, Shanghai, P.R. China;Department of Computer Science, Texas A&M University, College Station, TX;Department of Computer Science, Texas A&M University, College Station, TX
Venue:
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
Year:
2006

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Abstract

Abnormal variations of traffic are conventionally considered to occur under the condition that traffic rate is abnormally high in the cases, such as traffic congestions or traffic under distributed denial-of-service (DDOS) flood attacks. Various methods in detecting traffic variations at abnormally high rate have been reported. We note that a recent paper by Kuzmanovic and Knightly, which explains a type of DDOS attacks that may result in abnormally low traffic rate. Such a type of abnormal variations of traffic, therefore, can easily evade from detection systems based on abnormally high traffic rate. This paper presents a real-time and reliable detection approach to detect traffic variations at both abnormally high and low rates. The formulas in terms of detection probabilities, miss probabilities, classification criterion, and detection thre-sholds are proposed.