摘要
As a special type of distributed denial of service(DDoS) attacks, the low-rate DDoS(LDDoS) attacks have characteristics of low average rate and strong concealment, thus, it is hard to detect such attacks by traditional approaches. Through signal analysis, a new identification approach based on wavelet decomposition and sliding detecting window is proposed. Wavelet decomposition extracted from the traffic are used for multifractal analysis of traffic over different time scale. The sliding window from flow control technology is designed to identify the normal and abnormal traffic in real-time. Experiment results show that the proposed approach has advantages on detection accuracy and timeliness.
As a special type of distributed denial of service(DDoS) attacks, the low-rate DDoS(LDDoS) attacks have characteristics of low average rate and strong concealment, thus, it is hard to detect such attacks by traditional approaches. Through signal analysis, a new identification approach based on wavelet decomposition and sliding detecting window is proposed. Wavelet decomposition extracted from the traffic are used for multifractal analysis of traffic over different time scale. The sliding window from flow control technology is designed to identify the normal and abnormal traffic in real-time. Experiment results show that the proposed approach has advantages on detection accuracy and timeliness.
基金
supported by the Joint Funds of National Natural Science Foundation of China and Civil Aviation Administration of China (U1933108)
the National Science Foundation for Young Scientists of China (61601467)
the Key Program of Natural Science Foundation of Tianjin (17JCZDJC30900)
the Fundamental Research Funds for the Central Universities of China (3122019051).
