Research on DDOS attack detection method based on dynamic threshold

Xiaobo Tan; Zhongliang Zhang

doi:10.1117/12.2675156

25 May 2023 Research on DDOS attack detection method based on dynamic threshold

Xiaobo Tan, Zhongliang Zhang

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263623 (2023) https://doi.org/10.1117/12.2675156
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Distributed Denial of Service (DDOS) attack is a typical network attack. It paralyzes the target host through a large amount of resource occupation, which poses a great threat to Internet security. Since the birth of distributed denial of service (DDOS) attacks, a large number of them have been designed for DDOS detection every year. However, in the high-speed network era, they cannot control the complex and changing network environment. Therefore, this paper proposes a dynamic threshold detection method based on machine learning. This method processes network traffic in segments, analyzes the characteristics of a segment of network traffic and converts it into four traffic attributes, predicts the threshold of the next window by sliding window, and classifies data points in the window by using similarity. When all four attributes exceed the threshold range in a certain period of time, it is considered to be subject to DDOS attacks. At the same time, a freezing backtracking mechanism is proposed to prevent dynamic parameters from being polluted by attack traffic and speed up the generation of new thresholds after the attack. Compared with the traditional fixed threshold algorithm, this method has a very low false alarm rate for complex network environment, faster processing time, and significantly improved accuracy.

Citation Download Citation

Xiaobo Tan and Zhongliang Zhang "Research on DDOS attack detection method based on dynamic threshold", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263623 (25 May 2023); https://doi.org/10.1117/12.2675156

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