  The advent of the Internet of Things (IoT) has brought forth an era of
unprecedented connectivity, with an estimated 80 billion smart devices expected
to be in operation by the end of 2025. These devices facilitate a multitude of
smart applications, enhancing the quality of life and efficiency across various
domains. Machine Learning (ML) serves as a crucial technology, not only for
analyzing IoT-generated data but also for diverse applications within the IoT
ecosystem. For instance, ML finds utility in IoT device recognition, anomaly
detection, and even in uncovering malicious activities. This paper embarks on a
comprehensive exploration of the security threats arising from ML's integration
into various facets of IoT, spanning various attack types including membership
inference, adversarial evasion, reconstruction, property inference, model
extraction, and poisoning attacks. Unlike previous studies, our work offers a
holistic perspective, categorizing threats based on criteria such as adversary
models, attack targets, and key security attributes (confidentiality,
availability, and integrity). We delve into the underlying techniques of ML
attacks in IoT environment, providing a critical evaluation of their mechanisms
and impacts. Furthermore, our research thoroughly assesses 65 libraries, both
author-contributed and third-party, evaluating their role in safeguarding model
and data privacy. We emphasize the availability and usability of these
libraries, aiming to arm the community with the necessary tools to bolster
their defenses against the evolving threat landscape. Through our comprehensive
review and analysis, this paper seeks to contribute to the ongoing discourse on
ML-based IoT security, offering valuable insights and practical solutions to
secure ML models and data in the rapidly expanding field of artificial
intelligence in IoT.
