Enhancing IoT Security through Machine Learning: A Comprehensive Review and Future Directions

Abstract

The integration of machine learning (ML) into Internet of Things (IoT) systems presents transformative opportunities across various domains but also introduces numerous security challenges. This study explores the role of ML in enhancing IoT functionalities, particularly in data analytics, security, optimization, and user experience. Through a comprehensive review of literature, case studies, and expert interviews, the study highlights ML's potential to address IoT security challenges such as device heterogeneity, data privacy, network vulnerabilities, and device authentication. Moreover, the study identifies key methodologies for investigating the function of ML in IoT, including qualitative analysis and multi-method approaches, along with ethical considerations and limitations associated with integrating ML into IoT security frameworks. The study underscores the importance of ML in addressing IoT security challenges, emphasizing the need for privacy-preserving techniques, robust defenses against adversarial attacks, and interpretable ML models. Additionally, it identifies recommendations for future research, including the development of advanced cryptographic and federated learning approaches, robust defenses against adversarial attacks, and self-learning and adaptive ML techniques tailored to IoT environments' dynamic nature. Furthermore, the study suggests policy considerations for policymakers to ensure ethical considerations while encouraging innovation in the integration of ML into IoT systems.