Ridge-Based DPA: Improvement of Differential Power Analysis For Nanoscale Chips

Abstract

Differential power analysis (DPA), as a very practical type of side-channel attacks, has been widely studied and used for the security analysis of cryptographic implementations. However, as the development of the chip industry leads to smaller technologies, the leakage of cryptographic implementations in nanoscale devices tends to be nonlinear (i.e., leakages of intermediate bits are no longer independent) and unpredictable. These phenomena make some existing side-channel attacks not perfectly suitable, i.e., decreasing their performance and making some common used prior power models (e.g., Hamming weight) to be much less respected in practice. To solve the above issues, we introduce the regularization process from statistical learning to the area of side-channel attack and propose the ridge-based DPA. We also apply the cross-validation technique to search for the most suitable value of the parameter for our new attack methods. In addition, we present theoretical analyses to deeply investigate the properties of ridge-based DPA for nonlinear leakages. We evaluate the performance of ridge-based DPA in both simulation-based and practical experiments, comparing to the state-to-the-art DPAs. The results confirm the theoretical analysis. Further, our experiments show the robustness of ridge-based DPA to cope with the difference between the leakages of profiling and exploitation power traces. Therefore, by showing a good adaptability to the leakage of the nanoscale chips, the ridge-based DPA is a good alternative to the state-to-the-art ones.