The recently proposed TCP-targeted Low-rate Distributed Denial-of-Service (LDDoS) attacks send fewer packets to attack legitimate flows by exploiting the vulnerability in TCP's congestion control mechanism. They are difficult to detect while causing severe damage to TCP-based applications. Existing approaches can only detect the presence of an LDDoS attack, but fail to identify LDDoS flows. In this paper, we propose a novel metric -Congestion Participation Rate (CPR) -and a CPR-based approach to detect and filter LDDoS attacks by their intention to congest the network. The major innovation of the CPR-base approach is its ability to identify LDDoS flows. A flow with a CPR higher than a predefined threshold is classified as an LDDoS flow, and consequently all of its packets will be dropped. We analyze the effectiveness of CPR theoretically by quantifying the average CPR difference between normal TCP flows and LDDoS flows and showing that CPR can differentiate them. We conduct ns-2 simulations, test-bed experiments, and Internet traffic trace analysis to validate our analytical results and evaluate the performance of the proposed approach. Experimental results demonstrate that the proposed CPR-based approach is substantially more effective compared to an existing Discrete Fourier Transform (DFT)-based approach -one of the most efficient approaches in detecting LDDoS attacks. We also provide experimental guidance to choose the CPR threshold in practice.
In anomaly-based detection, Zhang et al. REF proposed a congestion-participation rate (CPR) metric and a CPR-based approach to detect and filter low-rate DDoS attacks.
Flow level detection and filtering of low-rate DDoS
Comput. Networks
Comput. Networks
Computer Science