Paper 2025/478

Attacking Single-Cycle Ciphers on Modern FPGAs featuring Explainable Deep Learning

Abstract

In this paper, we revisit the question of key recovery using side-channel analysis for unrolled, single-cycle block ciphers. In particular, we study the Princev2 cipher. While it has been shown vulnerable in multiple previous studies, those studies were performed on side-channel friendly ASICs or older FPGAs (e.g., Xilinx Virtex II on the SASEBO-G board), and using mostly expensive equipment. We start with the goal of exploiting a cheap modern FPGA and board using power traces from a cheap oscilloscope. Particularly, we use Xilinx Artix 7 on the Chipwhisperer CW305 board and PicoScope 5000A, respectively. We split our study into three parts. First, we show that the new set-up still exhibits easily detectable leakage, using a non-specific t-test. Second, we replicate attacks from older FPGAs. Namely, we start with the attack by Yli-Mäyry et al., which is a simple chosen plaintext correlation power analysis attack using divide and conquer. However, we demonstrate that even this simple, powerful attack does not work, demonstrating a peculiar behavior. We study this behavior using a stochastic attack that attempts to extract the leakage model, and we show that models over a small part of the state are inconsistent and depend on more key bits than what is expected. We also attempt classical template attacks and get similar results. To further exploit the leakage, we employ deep learning techniques and succeed in key recovery, albeit using a large number of traces. We perform the explainability technique called Key Guessing Occlusion (KGO) to detect which points the neural networks exploit. When we use these points as features for the classical template attack, although it did not recover the secret key, its performance improves compared to other feature selection techniques.