Presentation

DescriptionIn this paper, we focus on the portability issue in profiled side-channel attacks (SCAs) that arise due to significant device-to-device variations. Device discrepancy is inevitable in realistic attacks, but it is often neglected in research works. In this paper, we identify such device variations and take a further step towards leveraging the transferability of neural networks. We propose a novel adversarial learning-based profiled attack (AL-PA), which enables our neural network to learn device-invariant features. We evaluated our strategy on eight XMEGA microcontrollers. Without the need for target-specific preprocessing and multiple profiling devices, our approach has outperformed the state-of-the-art methods.