Machine Learning-Based Rowhammer Mitigation
Rowhammer is a security vulnerability that arises due to the undesirable electrical interaction between physically adjacent rows in DRAMs. Bit flips caused by Rowhammer can be exploited to craft many types of attacks in platforms ranging from edge devices to datacenter servers. Existing DRAM protections using error-correction codes and targeted row refresh are not adequate for defending against Rowhammer attacks. In this work, we propose a Rowhammer mitigation solution using machine learning (ML). We show that the ML-based technique can reliably detect and prevent bit flips for all the different types of Rowhammer attacks (including the recently proposed Half-double and Blacksmith attacks) considered in this work. Moreover, the ML model is associated with lower power and area overhead compared to recently proposed Rowhammer mitigation techniques, namely, Graphene and Blockhammer, for 40 different applications from the Parsec, Pampar, Splash-2, SPEC2006, and SPEC 2017 benchmark suites.
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Related Subject Headings
- Computer Hardware & Architecture
- 4607 Graphics, augmented reality and games
- 4009 Electronics, sensors and digital hardware
- 1006 Computer Hardware
- 0906 Electrical and Electronic Engineering
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Computer Hardware & Architecture
- 4607 Graphics, augmented reality and games
- 4009 Electronics, sensors and digital hardware
- 1006 Computer Hardware
- 0906 Electrical and Electronic Engineering