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ASPLOS2026顶会

APT: Securing Against DRAM Read Disturbance via Adaptive Probabilistic In-DRAM Trackers

Runjin Wu, Meng Zhang, You Zhou, Changsheng Xie, Fei Wu

2026年份
2被引次数
2顶会引用

摘要

With exacerbated DRAM read disturbance, transparent in-DRAM defenses require space (to track the aggressor rows) and time (to perform more mitigations). To reduce storage overhead, recent works have developed probabilistic row-sampling techniques with several entries. To address the time issue, these techniques employ Refresh Management (RFM) commands introduced in DDR5. However, probabilistic defenses with RFM face two critical challenges: (i) fixed-probability sampling under dynamic activation patterns can cause row-sampling misses, allowing attacks to evade mitigation, and (ii) RFM causes timing variations that can be exploited for side and covert channels to leak sensitive information. The goal of this paper is to design a low-cost and secure in-DRAM defense that overcomes these challenges.

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