Blockchain Security and Privacy using Machine Learning and Internet of Things-A Review

Technology connects the modern world. Countless endpoints, servers, infrastructures, and other services create a complex and fascinating cyber landscape that is always evolving, with new technologies being discovered every day and vulnerabilities being exposed. Blockchain, Internet of Things (IoT), and machine learning (ML) technologies have each developed strong answers to numerous cybersecurity issues in recent years. The creation of smarter safety devices that can automate tasks like resource allocation, anomaly or intrusion detection, and operational scalability has been made possible by ML and the IoT. Blockchain technology and ML both have advantages that can complement one another. While ML enables intelligent decision-making to identify anomalies, scalability, as well as effectiveness in Blockchain networks, blockchain delivers decentralized security and trust for the algorithms and data. This study demonstrates how blockchain technology and ML could work together to support 130 application cases pertaining to cybersecurity. Our research examines the present state of the art and explores cutting-edge advancements in blockchain security and privacy for IoT and ML, focusing on security aspects, challenges, and potential applications

I. Introduction

The integration of blockchain technology, machine learning (ML), and the Internet of Things (IoT) offers a groundbreaking approach to bolstering security and privacy in diverse applications. Blockchain's decentralized and tamper-proof ledger ensures data integrity and transparency, providing a solid base for secure data transactions. When paired with ML, this combination allows for sophisticated data analysis and predictive modeling to detect anomalies and potential security threats. IoT, which links numerous devices for data collection and exchange, gains enhanced security through blockchain's ability to handle data securely and immutably, ensuring reliable device communication. Collectively, these technologies facilitate comprehensive solutions for real-time threat detection, decentralized security management, and privacy preservation through advanced techniques such as zero-knowledge proofs and data anonymization. This interdisciplinary synergy is particularly beneficial in fields like smart homes, healthcare, supply chain management, and financial services, where protecting sensitive information and preventing fraud are paramount. By harnessing the strengths of block chain, ML, and IoT, this field addresses growing concerns about data security and privacy in a highly interconnected digital environment. This chapter explores the potential of blockchain for securing IoT environments, analyzing its advantages, limitations, and various applications in areas like supply chain management, smart cities, and healthcare. Additionally, the chapter investigates the key challenges hindering widespread blockchain adoption in IoT. As the world transitions from an information-sharing internet to a value-sharing one, blockchain emerges as a promising technology for facilitating trustworthy transactions, attracting significant interest from researchers and organizations alike. This study delves into the evolution of block chain, its security and privacy features, and its potential benefits and challenges across various domains. Blockchain technology offers innovative methods for operations, transactions, and trust building in public settings. It has applications in smart contracts, IoT smart grids, and cryptocurrency systems like Bitcoin. However, privacy and security remain a significant concern. Section 1 of this article provides an overview of blockchain security and privacy methods, including non-interactive zero-knowledge proof, hash chained storage, anonymous signatures, mixing protocols, and representative consensus algorithms. The remainder of this work is structured as follows: Section 2 gives a literature review on blockchain in IOT and ML, it contains a table summarizing different papers who have already done work in this. Finally, Section 3 concludes the paper.