Adversarial Machine Learning in IoT Security: A Comprehensive Survey

Abstract

This survey presents a comprehensive analysis of Adversarial Machine Learning (AML) as a critical approach for enhancing the security of Internet of Things (IoT) ecosystems, with a focus on its integration into Intrusion Detection Systems (IDSs). It synthesizes existing research, identifies current limitations, and outlines future directions for effective AML deployment. The survey explores emerging technologies to improve IDS robustness, adaptability, and threat anticipation capabilities. It further addresses the challenges of implementing AML in resource-constrained IoT environments. A structured, multi-phase attack framework inspired by the MITRE ATT&CK model is proposed to illustrate the evolving lifecycle of adversarial threats. Ethical and regulatory considerations are examined, emphasizing responsible deployment and accountability. The cross-domain applicability of AML is discussed across various environments, including smart grids, autonomous systems, cloud, and edge computing. Real-world use cases are analyzed, with attention to adversarial datasets and evaluation practices. Additionally, the role of Human-in-the-Loop (HITL) systems is highlighted for enhancing interpretability and trust in AML-powered IDSs. This survey serves as a valuable resource for researchers and practitioners aiming at advancing AML in securing IoT ecosystems with innovative, ethical, and practical defense strategies.