Enhancing Row-Sampling-Based Rowhammer defense methods with Machine Learning approach

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dc.contributor.authorMazurok, Valentyn
dc.contributor.authorLutsenko, Volodymyr
dc.date.accessioned2025-04-10T08:55:37Z
dc.date.available2025-04-10T08:55:37Z
dc.date.issued2024
dc.description.abstractThis paper investigates the integration of machine learning into the Row-Sampling technique to enhance its effectiveness in mitigating Rowhammer attacks in DRAM systems. A multidimensional multilabel predictor model is employed to dynamically predict and adjust probability thresholds based on real-time memory access patterns, improving the precision of row selection for targeted refresh. The approach demonstrates significant improvements in security, reducing Rowhammer-induced bit flips, while also maintaining energy efficiency and minimizing performance overhead. By leveraging machine learning, this work refines the Row-Sampling method, offering a scalable and adaptive solution to memory vulnerabilities in modern DRAM architectures.
dc.format.pagerangeP. 77-82
dc.identifier.citationMazurok, V. Enhancing Row-Sampling-Based Rowhammer defense methods with Machine Learning approach / Valentyn Mazurok, Volodymyr Lutsenko // Theoretical and Applied Cybersecurity: scientific journal. – 2024. – Vol. 6, No. 2. – P. 77-82. – Bibliogr.: 10 ref.
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132024.2.319008
dc.identifier.orcid0009-0006-2174-0800
dc.identifier.orcid0000-0001-7632-1730
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/73323
dc.language.isoen
dc.publisherIgor Sikorsky Kyiv Polytechnic Institute
dc.publisher.placeKyiv
dc.relation.ispartofTheoretical and Applied Cybersecurity: scientific journal, Vol. 6, No. 2
dc.subjectDRAM
dc.subjectRowhammer
dc.subjectmemory defense
dc.subjectmachine learning
dc.subject.udc004.33
dc.titleEnhancing Row-Sampling-Based Rowhammer defense methods with Machine Learning approach
dc.typeArticle

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 6, No. 2