Side channel analysis of an elliptic curve crypto-system based on multi-class classification

Cryptosystems, even after recent algorithmic improvements, can be vulnerable to side channel attacks (SCA). In this paper, we investigated one of the powerful class of SCA based on machine learning techniques in the forms of Principal Component Analysis (PCA) and multi-class classification. For this purpose, support vector machine (SVM) is investigated as a robust and efficient multi-class classifier along with a proper kernel function and its appropriate parameters. Our experiment performed on data leakage of FPGA implementation of elliptic curve cryptography (ECC), and the results, validated by cross validation approach, compare the efficiency of different kernel functions and the influence of function parameters.