Securing the Dynamic Realm: A Comprehensive Review of ML Algorithms in IoT-Based Home Automation Systems and Beyond

Abstract

This paper comprehensively reviews the imperative for secure IoT systems, emphasizing the challenges posed by their dynamic nature. Exploring various ML algorithms for IoT security, it highlights their advantages while addressing common limitations, including computational overhead and privacy risks. The focus narrows to federated learning (FL) and deep learning (DL) algorithms, showcasing their potential to overcome conventional ML drawbacks by preserving data privacy. The study provides an in-depth analysis of FL and DL-based techniques, emphasizing their efficiency in enhancing security in IoT-based home automation systems. The paper further examines ML's pivotal role in smart homes, presenting a case study that utilizes the support vector machine algorithm to distinguish between regular occupants and intruders. Extending the discussion to face recognition for home automation, the review underscores the utilization of IoT and smart techniques. Beyond home automation, the paper delves into the broader landscape of ML applications in the Fourth Industrial Revolution, offering insights into cybersecurity, smart cities, healthcare, and more. The review briefly introduces the utilization of Convolutional Neural Networks (CNNs) within the broader context of deep learning algorithms. While the main emphasis remains on FL and DL, the paper acknowledges CNNs as a powerful tool for image-based tasks, especially relevant in the context of visual data analysis for security in IoT-based home automation systems. In summary, this concise review encapsulates the transformative impact of ML on IoT-based home automation security, providing valuable perspectives on current trends, challenges, and future research directions. The inclusion of CNNs within the abstract recognizes their relevance, especially in image-based security applications.