engleski

Traditional Machine Learning Methods for Side- Channel Analysis

Traditional machine learning techniques (excluding deep learning) include a range of approaches, such as supervised, semi-supervised, and unsupervised modeling methods, often coupled with data augmentation and dimensionality reduction. The aim of this chapter is to provide an overview of the application of traditional machine learning methods in the field of side-channel analysis. The chapter encompasses the common methods used in side-channel attacks, a historical overview of the use of machine learning methods in side-channel analysis, and a brief description of various machine learning approaches that have been used in related studies. Both machine learning methods and side-channel specific methods such as Principal Component Analysis, Linear Discriminant Analysis, Template Attacks, Random Forests, Multilayer Perceptron and many others are compared and the current status of their use in side-channel analysis is presented. Several research avenues are still incomplete and the chapter points out some of the open questions.

machine learning ; side-channel analysis ; data mining

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