Learning to Mitigate Rowhammer Attacks

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Joardar, BK; Bletsch, TK; Chakrabarty, K

Published Date

  • January 1, 2022

Published In

  • Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, Date 2022

Start / End Page

  • 564 - 567

International Standard Book Number 13 (ISBN-13)

  • 9783981926361

Digital Object Identifier (DOI)

  • 10.23919/DATE54114.2022.9774703

Citation Source

  • Scopus