On (in)Security of Edge-based Machine Learning Against Electromagnetic Side-channels

Abstract

Machine (deep) learning represents mainstream research and development direction. This success can be linked to the ever-increasing computational capabilities and vast amounts of available data, resulting in ever more sophisticated machine learning models. The design and training of such machine learning models are challenging and expensive tasks, which incentivize companies to protect and keep it secret. Additionally, the wide applicability of machine learning results in diverse deployment scenarios. Many machine learning architectures are deployed on edge devices, such as sensors or actuators, making them susceptible to side-channel attacks. This work surveys the research works considering electromagnetic side-channel and edge-based machine learning models. After discussing state-of-the-art attacks and countermeasures, we propose several open problems to be investigated in future research.