Paper 2023/021

DLFA: Deep Learning based Fault Analysis against Block Ciphers

Abstract

The proliferation of embedded cryptographic devices in the Internet of Things (IoT) ecosystem has elevated the importance of physical security assessments. Although traditional Fault Analysis (FA) methods exhibit significant effectiveness in cryptographic key recovery, their practical application is heavily constrained by rigid mathematical requirements, the demand for precise physical fault injection, and a sensitivity to measurement noise. To address these limitations, this paper proposes Deep Learning-based Fault Analysis (DLFA), a comprehensive attack framework. By decomposing cryptanalysis into tailored feature engineering and neural network classification, DLFA successfully unifies four prominent fault models (i.e., Differential (DFA), Statistical (SFA), Statistical Ineffective (SIFA), and Persistent Fault Analysis (PFA)) under a single data-driven paradigm. Extensive physical evaluations on the SAKURA-G FPGA implementing AES-128 demonstrate that DLFA reduces the data complexity and computational time overhead compared to classical algebraic solvers. More crucially, DLFA exhibits sustained analytical stability against severe physical injection noise, relaxing the stringent hardware requirements for attackers. Finally, we employ the Integrated Gradients (IG) principle to conduct a quantitative attribution analysis, proving that the neural networks autonomously learn valid cryptographic leakages rather than overfitting to experimental artifacts.