ProTRR: Principled yet Optimal In-DRAM Target Row Refresh
Michele Marazzi, Patrick Jattke, Flavien Solt, Kaveh Razavi
摘要
The DRAM substrate is becoming increasingly more vulnerable to Rowhammer as we move to smaller technology nodes. We introduce ProTRR, the first principled in-DRAM Target Row Refresh mitigation with formal security guarantees and low bounds on overhead. Unlike existing proposals that require changes to the memory controllers, the in-DRAM nature of ProTRR enables its seamless integration. However, this means that ProTRR must respect the synchronous nature of the DRAM protocol, which limits the number of DRAM rows that can be protected at any given time. To overcome this challenge, ProTRR proactively refreshes each row that is most likely to observe bit flips in the future. While this strategy catches the rows that are hammered the most, some others may still fly under the radar. We use this observation to construct Feinting, a new Rowhammer attack that we formally prove to be optimal in this setting. We then conFigure ProTRR to be secure against Feinting. To achieve this, ProTRR should keep track of accesses to each row, which is prohibitively expensive to implement in hardware. Instead, ProTRR uses a new frequent item counting scheme that leverages Feinting to provide a provably optimal yet flexible trade-off between the tolerated DRAM vulnerability, the number of counters, and the number of additional refreshes. Our extensive evaluation using an ASIC implementation of ProTRR and cycle-accurate simulation shows that ProTRR can provide principled protection for current and future DRAM technologies with a negligible performance, power, and area impact. ProTRR is fully compatible with DDR4 and the new Refresh Management (RFM) extension in DDR5.
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引用它的顶会 Paper53
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它引用的顶会 Paper18
- DRAMA: Exploiting DRAM Addressing for Cross-CPU AttacksPeter Pessl, Daniel Gruss, Clémentine Maurice, Michael Schwarz 等USENIX Security 2016 · 被引用 500 次
- Drammer: Deterministic Rowhammer Attacks on Mobile PlatformsVictor van der Veen, Yanick Fratantonio, Martina Lindorfer, Daniel Gruss 等CCS 2016 · 被引用 381 次
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