Machine Learning in Side-Channel Analysis
Electronic devices may undergo attacks going beyond traditional cryptanalysis. Side-channel analysis is an alternative attack that exploits information leaking from physical implementations of e.g. cryptographic devices in order to discover cryptographic keys or other secrets. This paper comprehensively investigates the application of a machine learning technique in side-channel analysis. The considered technique is a powerful kernel-based learning algorithm: the Least Squares Support Vector Machine (LS-SVM). The chosen side-channel is the power consumption and the target is a software implementation of the advanced encryption standard.