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BT-MTD: Bus Traversal-based Moving Target Defense for Smart Grid

arXiv Security Archived Jun 16, 2026 ✓ Full text saved

arXiv:2606.15047v1 Announce Type: new Abstract: Moving Target Defense (MTD) is a proactive security strategy designed to enhance cyber-resilience by dynamically altering system parameters, thereby preventing adversaries from acquiring the critical information needed to execute stealth attacks. In this paper, we consider the case in which the operator modifies the admittance of branches to enable MTD, and focus on the problem of effectively protecting the system with fewer number of branch admitt

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✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 13 Jun 2026] BT-MTD: Bus Traversal-based Moving Target Defense for Smart Grid Jingyi Yan, Ke Sun, Zhenglin Li, Hongying Jia Moving Target Defense (MTD) is a proactive security strategy designed to enhance cyber-resilience by dynamically altering system parameters, thereby preventing adversaries from acquiring the critical information needed to execute stealth attacks. In this paper, we consider the case in which the operator modifies the admittance of branches to enable MTD, and focus on the problem of effectively protecting the system with fewer number of branch admittance modifications and shorter computational time. Specifically, we identify the ineffectual branches whose admittance modification do not contribute to the improvement of MTD effectiveness via theoretical analysis. Building on these insights, we propose the Bus Traversal-based MTD (BT-MTD), which is a bus-oriented algorithm that traverses over the buses of the network according to analytically derived guidelines. The performance of the BT-MTD is evaluated and compared with four existing strategies on standard IEEE test systems, demonstrating its robustness and superior performance in effectiveness, efficiency, and computational cost. The code of BT-MTD is available at: this https URL. Subjects: Cryptography and Security (cs.CR); Systems and Control (eess.SY) Cite as: arXiv:2606.15047 [cs.CR]   (or arXiv:2606.15047v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.15047 Focus to learn more Submission history From: Ke Sun [view email] [v1] Sat, 13 Jun 2026 01:31:24 UTC (571 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 16, 2026
    Archived
    Jun 16, 2026
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