DDoS attack detection and wavelets

This paper presents a systematic method for DDoS attack detection. DDoS attack can be considered system anomaly or misuse from which abnormal behavior is imposed on network traffic. Attack detection can be performed via abnormal behavior identification. Network traffic characterization with behavior modeling could be a good guidance of attack detection. Aggregated traffic has been found to be strong bursty across a wide range of time scales. Wavelet analysis is able to capture complex temporal correlation across multiple time scales with very low computational complexity. We utilize energy distribution based on wavelet analysis to detect DDoS attack traffic. Energy distribution over time would have limited variation if the traffic keeps its behavior over time (i.e. attackfree situation); while an introduction of attack traffic in the network would elicit significant energy distribution deviation in short time period. Our experimental results with typical Internet traffic trace show that energy distribution variance changes markedly causing a “spike” when traffic behaviors affected by DDoS attack. In contrast, normal traffic exhibits a remarkably stationary energy distribution. In addition, this spike in energy distribution variance can be captured in early stage of attack, far ahead of congestion build-up, making it an effective attack detection.