Real-time Detection of DDoS Attacks Based on Hurst Index

• Published in: 2022 2nd International Conference on Networking Systems of AI (INSAI) pp. 42 - 45
• Main Authors: Ling, Ying, Yang, Chunyan, Li, Xin, Xie, Ming, Ming, Shaofeng, Lu, Jieke, Tang, Fuchuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2022
• Subjects: DDoS Attack Technology; Detection Technology; Hurst Index; Real-time Detection
• DOI: 10.1109/INSAI56792.2022.00018

DDoS is considered as the most dangerous attack and threat to software defined network (SDN). The existing mitigation technologies include flow capacity method, entropy method and flow analysis method. They rely on traffic sampling to achieve true real-time inline DDoS detection accuracy. However, the cost of the method based on traffic sampling is very high. Early detection of DDoS attacks in the controller is very important, which requires highly adaptive and accurate methods. Therefore, this paper proposes an effective and accurate real-time DDoS attack detection technology based on hurst index. The main detection methods of DDoS attacks and the traffic characteristics when DDoS attacks occur are briefly analyzed. The Hurst exponent estimation method and its application in real-time detection (RTD) of DDoS attacks are discussed. Finally, the simulation experiment test analysis is improved to verify the effectiveness and feasibility of RTD of DDoS attacks based on hurst index.