Integrating IoT, Machine Learning, and Blockchain for Enhanced Network Security and Efficiency: A Review

Abstract

The rapid expansion of the Internet of Things (IoT) has led to significant improvements in automation, communication, and data exchange across various industries. However, the integration of numerous interconnected devices has also heightened concerns regarding network security, data privacy, and system efficiency. This review explores the intersection of IoT, machine learning (ML), and blockchain technology as a holistic approach to enhancing network security and operational efficiency. IoT devices generate vast amounts of data, which, when combined with machine learning algorithms, can improve predictive analytics, anomaly detection, and real-time decision-making. Blockchain technology further strengthens this ecosystem by providing a decentralized and immutable ledger, ensuring secure data transmission and reducing vulnerabilities. The paper discusses recent advancements, key challenges, and future research directions, highlighting the potential of combining IoT, ML, and blockchain to create robust, secure, and efficient networks. Specific use cases, such as in healthcare, supply chain management, and smart cities, are also examined to illustrate practical implementations. This review concludes with insights into the future potential of these technologies and the challenges that remain in their integration.