*3.2.7.3 Machine learning attacks*

Machine learning is an emerging approach to side-channel attacks. Although numerous algorithms can potentially be used, the specific feature selection and data set size have the major influence on the success of the attack. Examples of approaches are supervised learning, support vector machines, random forest, neural networks and unsupervised learning. To date, most research has focussed on support vector machines [34–36], random forest [37] and neural networks [38]. Countermeasures to machine learning may include higher order masking approaches and the use of poisoned data.
