Exploiting Support Vector Machine Algorithm to Break the Secret Key

Abstract

Template attacks (TA) and support vector machine (SVM) are two effective methods in side channel attacks (SCAs). Almost all studies on SVM in SCAs assume the required power traces are sufficient, which also implies the number of profiling traces belonging to each class is equivalent. Indeed, in the real attack scenario, there may not be enough power traces due to various restrictions. More specifically, the Hamming Weight of the S-Box output results in 9 binomial distributed classes, which significantly reduces the performance of SVM compared with the uniformly distributed classes. In this paper, the impact of the distribution of profiling traces on the performance of SVM is first explored in detail. And also, we conduct Synthetic Minority Oversampling TEchnique (SMOTE) to solve the problem caused by the binomial distributed classes. By using SMOTE, the success rate of SVM is improved in the testing phase, and SVM requires fewer power traces to recover the key. Besides, TA is selected as a comparison. In contrast to what is perceived as common knowledge in unrestricted scenarios, our results indicate that SVM with proper parameters can significantly outperform TA.