Research on Fault Probability Based on Hamming Weight in Fault Injection Attack

Fault attacks have emerged as an increasingly effective approach for integrated circuit security attacks due to their short execution time and minimal data requirement. However, the lack of a unified leakage model remains a critical challenge, as existing methods often rely on algorithm-specific details or prior knowledge of plaintexts and intermediate values. This paper proposes the Fault Probability Model based on Hamming Weight (FPHW) to address this. This novel statistical framework quantifies fault attacks by solely analyzing the statistical response of the target device, eliminating the need for attack algorithm details or implementation specifics. Building on this model, a Fault Injection Attack method based on Mutual Information (FPMIA) is introduced, which recovers keys by leveraging the mutual information between measured fault probability traces and simulated leakage derived from Hamming weight, reducing data requirements by at least 44% compared to the existing Mutual Information Analysis method while achieving a high correlation coefficient of 0.9403 between measured and modeled fault probabilities. Experimental validation on an AES-128 implementation via a Microcontroller Unit demonstrates that FPHW accurately captures the data dependence of fault probability and FPMIA achieves efficient key recovery with robust noise tolerance, establishing a unified and efficient framework that surpasses traditional methods in terms of generality, data efficiency, and practical applicability.