Empirical performance evaluation of machine learning based DDoS attack detections

Abstract

A distributed denial-of-service attack (DDoS) is a critical attack-type that strongly damages the Quality of Service (QoE). Although various novel security technologies have been continually developing, completely preventing DDoS threats is still unreached. Hence, applying deep learning to detect DDoS attacks effectively is high interest. However, comprehensively analyzing these techniques remains unobservant. In this paper, we present a solid architecture supporting evaluating machine-learning-based DDoS detection techniques from both public and self-generated datasets. A high-accuracy ensemble DDoS detection method is proposed from the evaluation results. Furthermore, we expect that these results could be essential resources for later DDoS researches. Furthermore, the study also provides an overview of the features, labels from which there is a basis for creating a complete dataset used for DDoS …