2021/05/04 by Raphaël Joud, Pierre-Alain Moëllic, Joud, Raphaël +6
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Security and Verification in Computing #cs.AI #cs.CR
paper · pdf · doi:10.48550/arxiv.2105.01401
Accepted at 7th IEEE World Forum on Internet of Things (2021)
arxiv created 2021/05/04 · openalex publication_date 2021/05/04 · arxiv updated 2021/05/05 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Utilization of Machine Learning (ML) algorithms, especially Deep Neural Network (DNN) models, becomes a widely accepted standard in many domains more particularly IoT-based systems. DNN models reach impressive performances in several sensitive fields such as medical diagnosis, smart transport or security threat detection, and represent a valuable piece of Intellectual Property. Over the last few years, a major trend is the large-scale deployment of models in a wide variety of devices. However, this migration to embedded systems is slowed down because of the broad spectrum of attacks threatening the integrity, confidentiality and availability of embedded models. In this review, we cover the landscape of attacks targeting the confidentiality of embedded DNN models that may have a major impact on critical IoT systems, with a particular focus on model extraction and data leakage. We highlight the fact that Side-Channel Analysis (SCA) is a relatively unexplored bias by which model's confidentiality can be compromised. Input data, architecture or parameters of a model can be extracted from power or electromagnetic observations, testifying a real need from a security point of view.