Multi-Sensor Data Fusion for Enhanced Detection of Laser Fault Injection Attacks in Cryptographic Hardware: Practical Results
Mohammad Ebrahimabadi, Raphael Viera, Sylvain Guilley, Jean‐Luc Danger, Jean-Max Dutertre, Naghmeh Karimi · 2025
Though considered secure the cryptographic hardware can be compromised by fault injection attack, especially laser illumination due to its precision in targeting specific areas and its fine temporal control. To address this threat, this paper presents a low-cost detection scheme that utilizes Time-to-Digital Converters (TDCs) to sense the IR drops induced by laser illumination. To achieve a high detection rate while minimizing false alarms, the proposed approach incorporates multiple sensors, with as few as two sensors demonstrated in the study. The effectiveness of the scheme is validated using a real laser setup to illuminate a targeted AES module implemented on an AMD/Xilinx Artix-7 FPGA.