Side channel information analysis based on machine learning

Cryptographic devices, even after recent improvements, are still vulnerable to side channel attacks(SCA). The majority of the available literature of SCA belongs to the traditional methods such as simple and differential analysis methods and template attacks, whilst few studies based on machine learning are available. In this paper, we investigate the side channel analysis based on machine learning techniques in the form of principal component analysis (PCA) and support vector machine (SVM). For this purpose, we verify the efficiency of RBF and POLY kernel functions of SVM classifier under the influence of the number of principal components (PCs). Our experimental results, obtained by cross validation method, comprise the accuracy and computational complexity of this method and can show the validity and the effectiveness of the proposed approach.