Year: 2024
Type: article
Abstract: With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks.
Source: Electronics
Authors: Kai Xu, Zemin Li, Nan Liang, Fanchun Kong, Shaobo Lei, Shengjie Wang, Agyemang Paul, Zhefu Wu
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4402417403