pacSCA: A Profiling-Assisted Correlation-based Side-Channel Attack on GPUs
Xin Wang, Wei Zhang · 2020
GPUs which serve as graphic-oriented computation platform are now gradually used to solve a range of compute-intensive and data-parallel scientific computing problems which can be perfectly parallelized for performance speedups. Particularly, GPUs have recently become popular to host the encryption/decryption algorithms due to its high-throughput computing capability. However, the security issues of moving the cryptographic algorithms onto GPUs have not been studied adequately. Consequently, with absence of any protection strategy, the potential vulnerabilities of GPUs to side-channel attacks may expose the confidential information with high risk. In this paper, we propose a Profiling-Assisted Correlation-based Side-Channel Attack (pacSCA) to demonstrate that ignoring security issues and naively moving security services onto GPUs can offer adversaries fatal vulnerabilities to thieve critical information. The results show that the proposed SCA can rebuild the secure key of the AES-128 algorithm in less than 6 seconds, revealing the urgency of protecting GPUs against side-channel threats.