Security Concerns of Machine Learning Hardware

AI-as-a-Service (AIaaS) has been emerging with model providers deploying their models on cloud and model consumers using the model. Recently, ML models are being deployed on edge devices to improve cost and response time. The widespread usage of machine learning has made the study of security in the context of Machine Learning (ML) very critical. Model extraction attacks focuses on extracting model parameters such as weights and biases which can be used to clone a ML target model deployed on the cloud or on an edge device hardware. This paper explores different types of attacks on ML models primarily focusing on model extraction attacks on ML hardware such as scan-chain and side-channel attacks. The paper present an analysis of various such attacks and their countermeasures. Possible future directions of work are also discussed.