Towards Human Dependency Elimination: AI Approach to SCA Robustness Assessment

Evaluating the side-channel resistance in practice is a problematic and arduous process. Current certification schemes require to attack the device under test with an ever-growing number of techniques to validate its security. In addition, the success or failure of these techniques strongly depends on the individual implementing them due to the fallible and human intrinsic nature of several steps of this path. To alleviate this problem, we propose a battery of automated (Estimation of Distribution Algorihm(EDA)-based) attacks as a side-channel analysis robustness assessment of an embedded device. To prove our approach, we conduct realistic experiments on two different devices, creating a new dataset (AES_RA) as a part of our contribution. Furthermore, in this context of automation, we propose several novel improvements over current EDA-based attacks, as follows: 1) optimization of the search process by employing two proposed initialization techniques; 2) improvement and analysis of the generalization of the obtained templates; 3) acceleration of the search process by combining EDAs with Principal Component Analysis (PCA). The last contribution also serves as an alternative way of selecting optimal principal components automatically. We support our claims with experiments on AES_RA and a public dataset (ASCAD), showing how our, although fully automated, approach can straightforwardly provide state-of-the-art results.