Learning to Mitigate Rowhammer Attacks

Rowhammer is a vulnerability that arises due to the undesirable interaction between physically adjacent rows in DRAMs. Existing DRAM protections are not adequate to defend against Rowhammer attacks. 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 considered here. Moreover, the ML model is associated with lower power and area overhead compared to recently proposed Rowhammer mitigation techniques for 26 different applications from the Parsec, Pampar, and Splash-2 benchmark suites.