DRAM-Locker: A General-Purpose DRAM Protection Mechanism Against Adversarial DNN Weight Attacks
Document Type
Conference Proceeding
Publication Date
1-1-2024
Abstract
In this work, we propose DRAM-Locker as a robust general-purpose defense mechanism that can protect DRAM against various adversarial Deep Neural Network (DNN) weight attacks affecting data or page tables. DRAM-Locker harnesses the capabilities of in-DRAM swapping combined with a lock-table to prevent attackers from singling out specific DRAM rows to safeguard DNN's weight parameters. Our results indicate that DRAM-Locker can deliver a high level of protection downgrading the performance of targeted weight attacks to a random attack level. Furthermore, the proposed defense mechanism demonstrates no reduction in accuracy when applied to CIFAR-I0 and CIFAR-100. Importantly, DRAM-Locker does not necessitate any software retraining or result in extra hardware burden.
Identifier
85210067901 (Scopus)
ISBN
[9798350348590]
Publication Title
Proceedings -Design, Automation and Test in Europe, DATE
External Full Text Location
https://doi.org/10.23919/date58400.2024.10546892
ISSN
15301591
Grant
2228028
Fund Ref
National Science Foundation
Recommended Citation
Zhou, Ranyang; Ahmed, Sabbir; Roohi, Arman; Rakin, Adnan Siraj; and Angizi, Shaahin, "DRAM-Locker: A General-Purpose DRAM Protection Mechanism Against Adversarial DNN Weight Attacks" (2024). Faculty Publications. 798.
https://digitalcommons.njit.edu/fac_pubs/798
