A Detection Scheme for Multiplexed Asymmetric Workload DDoS Attacks in High-Speed Networks
Fuhao Yang, Hua Wu, Xiangyu Zheng, Jinfeng Chen, Xiaoyan Hu, Jing Ren · IEEE Transactions on Network and Service Management · 2025
The asymmetric workload attack is an application layer attack that aims to exhaust the Central Processing Unit (CPU) resources of a server. Some attackers exploit new features of the Hypertext Transfer Protocol version 2 (HTTP/2) to launch Multiplexed Asymmetric Workload DDoS (MAWD) attacks using a small number of bots, which can cause denial of service on HTTP/2 servers. Data centers in high-speed networks host a large number of web applications. However, most of the detection methods for asymmetric workload attacks rely on request semantic analysis, which cannot be applied to encrypted MAWD attack traffic in high-speed networks. Besides, traditional rate-based DDoS detection methods are ineffective in detecting MAWD because the MAWD attacks use legitimate HTTP requests, and HTTP/2 traffic is bursty in nature. This paper proposes a practical scheme to detect MAWD attacks in high-speed networks. We construct an effective feature set based on the characteristics of MAWD attacks in high-speed networks and design MAWD-HashTable (MAWD-HT) to extract features quickly. Experimental results on real traffic traces with speeds reaching Gbps demonstrate that our scheme can detect MAWD attacks within 3 seconds, with a recall rate of more than 99%, a FPR of less than 0.1%, and an acceptable resource consumption.