2024

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Enhancing Row-Sampling-Based Rowhammer defense methods with Machine Learning approach
(Igor Sikorsky Kyiv Polytechnic Institute, 2024) Mazurok, Valentyn; Lutsenko, Volodymyr
This 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.