Real-Time Detection of Stealthy DDoS Attacks Using Time-Series Decomposition

Recently, many new types of distributed denial of service (DDoS) attacks have emerged, posing a great challenge to intrusion detection systems. In this paper, we introduce a new type of DDoS attacks called stealthy DDoS attacks, which can be launched by sophisticated attackers. Such attacks are different from traditional DDoS attacks in that they cannot be detected by previous detection methods effectively. In response to this type of DDoS attacks, we propose a detection approach based on time-series decomposition, which divides the original time series into trend and random components. It then applies a double autocorrelation technique and an improved cumulative sum technique to the trend and random components, respectively, to detect anomalies in both components. By separately examining each component and synthetically evaluating the overall results, the proposed approach can greatly reduce not only false positives and negatives but also detection latency. In addition, to make our method more generally applicable, we apply an adaptive sliding-window to our real-time algorithm. We evaluate the performance of the proposed approach using real Internet traces, demonstrating its effectiveness.