E-DDoS: An Evaluation System for DDoS Attack Detection

Research in the area of Distributed Denial of Service (DDoS) attack detection is of paramount importance in the field of network security. Many existing studies employ static evaluation methods that fail to account for the reduction in accuracy due to the impact of inference latency on the timeliness of classification results. Furthermore, these studies frequently rely on simulated datasets for experimentation, which often lack the complexity and challenge of real-world attacks. These limitations significantly hinder the applicability of such research in practical scenarios. To overcome these challenges, we propose an evaluation methodology for real-time DDoS attack detection incorporating inference latency considerations. Additionally, we have developed a challenging DDoS dataset named THU-DDoS2024 and conducted experiments across four classification algorithms. This novel evaluation method and the newly generated dataset are integrated into an evaluation framework named E-DDoS. Leveraging E-DDoS, the “Intelligent Classification of High-Speed Network Traffic (ICNT)”, Grand Challenge was initiated. This event aims to motivate both academic and industrial sectors to delve into high-speed traffic classification tasks, thereby enhancing the applicability of research outputs to real-world applications.