Author
Listed:
- Bing Zhang
(School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou 545006, China
Liuzhou Key Laboratory of Big Data Intelligent Processing and Security, Liuzhou 545006, China
Cybersecurity Monitoring Center for Guangxi Education System, Liuzhou 545006, China)
- Hui Li
(School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou 545006, China
Liuzhou Key Laboratory of Big Data Intelligent Processing and Security, Liuzhou 545006, China
Cybersecurity Monitoring Center for Guangxi Education System, Liuzhou 545006, China)
- Shuai Zhang
(Liuzhou Key Laboratory of Big Data Intelligent Processing and Security, Liuzhou 545006, China
School of Science, Guangxi University of Science and Technology, Liuzhou 545006, China
School of Automotive and Information Engineering, Guangxi Eco-Engineering Vocational and Technical College, Liuzhou 545004, China)
- Jing Sun
(School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou 545006, China
Liuzhou Key Laboratory of Big Data Intelligent Processing and Security, Liuzhou 545006, China
Cybersecurity Monitoring Center for Guangxi Education System, Liuzhou 545006, China)
- Ning Wei
(Liuzhou Key Laboratory of Big Data Intelligent Processing and Security, Liuzhou 545006, China
Cybersecurity Monitoring Center for Guangxi Education System, Liuzhou 545006, China
School of Automotive and Information Engineering, Guangxi Eco-Engineering Vocational and Technical College, Liuzhou 545004, China)
- Wenhong Xu
(School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou 545006, China
Liuzhou Key Laboratory of Big Data Intelligent Processing and Security, Liuzhou 545006, China
Cybersecurity Monitoring Center for Guangxi Education System, Liuzhou 545006, China)
- Huan Wang
(School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou 545006, China
Liuzhou Key Laboratory of Big Data Intelligent Processing and Security, Liuzhou 545006, China
Cybersecurity Monitoring Center for Guangxi Education System, Liuzhou 545006, China)
Abstract
Path hopping serves as an active defense mechanism in network security, yet it encounters challenges like a restricted path switching space, the recurrent use of similar paths and vital nodes, a singular triggering mechanism for path switching, and fixed hopping intervals. This paper introduces an active defense method employing multiple constraints and strategies for path hopping. A depth-first search (DFS) traversal is utilized to compute all possible paths between nodes, thereby broadening the path switching space while simplifying path generation complexity. Subsequently, constraints are imposed on residual bandwidth, selection periods, path similitude, and critical nodes to reduce the likelihood of reusing similar paths and crucial nodes. Moreover, two path switching strategies are formulated based on the weights of residual bandwidth and critical nodes, along with the calculation of path switching periods. This facilitates adaptive switching of path hopping paths and intervals, contingent on the network’s residual bandwidth threshold, in response to diverse attack scenarios. Simulation outcomes illustrate that this method, while maintaining normal communication performance, expands the path switching space effectively, safeguards against eavesdropping and link-flooding attacks, enhances path switching diversity and unpredictability, and fortifies the network’s resilience against malicious attacks.
Suggested Citation
Bing Zhang & Hui Li & Shuai Zhang & Jing Sun & Ning Wei & Wenhong Xu & Huan Wang, 2024.
"Multi-Constraint and Multi-Policy Path Hopping Active Defense Method Based on SDN,"
Future Internet, MDPI, vol. 16(4), pages 1-21, April.
Handle:
RePEc:gam:jftint:v:16:y:2024:i:4:p:143-:d:1380283
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