Power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques

Systems that use secret keys or personal details are seriously at risk from side-channel attacks, especially if they rely on power analysis. Attackers can use unintentional sources like power consumption and electromagnetic waves to extract sensitive information. Recently, machine learning has become a promising approach for executing power side-channel attacks that are efficient and effective for single and cross-device environments. This paper reviews various machine learning-based power side-channel attacks, including feature extraction techniques, classification methods, and countermeasures. This survey investigates same-device and cross-device attacks that use multiple devices for training an artificial intelligence model for this purpose. It examines the strengths and limitations of various machine learning algorithms and suggests areas for future research to address challenges.