Machine Learning-Based Rowhammer Mitigation

Abstract

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.