Mazurok, ValentynLutsenko, Volodymyr2025-04-102025-04-102024Mazurok, 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.https://ela.kpi.ua/handle/123456789/73323This 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.enDRAMRowhammermemory defensemachine learningArticleP. 77-82https://doi.org/10.20535/tacs.2664-29132024.2.319008004.330009-0006-2174-08000000-0001-7632-1730