A REVIEW OF SECURING INTERNET OF THINGS (IOT) WITH MACHINE LEARNING

Abstract

The Internet of Things (IoT) has revolutionized the way we interact with the physical world, embedding everyday objects with sensors and connectivity to enhance efficiency and convenience. However, the rapid proliferation of IoT devices has raised significant concerns regarding security and privacy. Traditional security mechanisms often fall short in addressing the dynamic and diverse nature of IoT ecosystems. This paper explores the paradigm shift towards securing IoT through the integration of Machine Learning (ML) techniques. This research delves into the innovative fusion of IoT and ML, presenting a comprehensive analysis of how machine learning algorithms can fortify the security infrastructure of IoT networks. By leveraging ML algorithms, IoT systems can detect and respond to evolving cyber threats in real-time. This proactive approach enhances anomaly detection, intrusion prevention, and incident response capabilities, mitigating potential risks before they escalate. The study discusses various ML models such as deep learning, clustering, and reinforcement learning, elucidating their applications in IoT security. Deep learning algorithms, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are examined for their prowess in analyzing intricate patterns within large datasets, ensuring the integrity of IoT data transmission. Additionally, clustering algorithms are explored for their efficiency in grouping similar IoT devices, enabling the implementation of tailored security protocols for specific device clusters. Reinforcement learning techniques are also investigated to optimize security strategies dynamically, adapting to evolving threats in real-time. Furthermore, the paper sheds light on the challenges and opportunities in the integration of ML with IoT security. Ethical considerations, data privacy, and the energy efficiency of ML algorithms are discussed in the context of resource-constrained IoT devices. The research also explores the potential of federated learning, enabling collaborative ML model training across distributed IoT networks without compromising data privacy.