AbstractSide-channel attacks (SCAs) are powerful attacks based on the information obtained from the implementation of cryptographic devices. Profiling side-channel attacks has received a lot of attention in recent years due to the fact that this type of attack defines the worst-case security assumptions. The SCA community realized that the same approach is actually used in other domains in the form of supervised machine learning. Consequently, some researchers started experimenting with different machine learning techniques and evaluating their effectiveness in the SCA context. More recently, we are witnessing an increase in the use of deep learning techniques in the SCA community with strong first results in side-channel analyses, even in the presence of countermeasures. In this chapter, we consider the evolution of profiling attacks, and subsequently we discuss the impacts they have made in the data preprocessing, feature engineering, and classification phases. We also speculate on the future directions and the best-case consequences for the security of small devices.