A review on blockchain-based secure data transmission in IoT networks using machine learning

Abstract

The Internet of Things (IoT) generally provides details on every online object. It manages the operations remotely and monitors them without human intervention. It can adjust to the circumstances either instantly or via experience. Users’ security and privacy are now top concerns due to the widespread use of IoT devices in many applications. Cyber-attacks are eroding the current security and privacy rules exponentially. Recently, Blockchain technology emerged as one of the IoT&s;s most sophisticated methods. Blockchain technology was created to enable digital transactions and offer dependable, secure access to distributed ledgers. Blockchain technology was created to enable digital transactions and offer dependable and secure distributed ledger accessibility. Thanks to blockchain technology, trustworthy third parties are not necessary for secure transactions between users. The blockchain will record the rate at which each node transmits data packets. Consequently, Machine Learning (ML) methods are implemented to generate accurate outputs from extensive and intricate databases, thereby facilitating prediction and detecting vulnerabilities in IoT-based systems. It is possible to gather data from heterogeneous sensors by converting the various value types for different sensors. It is not anticipated to attain its peak level in addition to the rapid spread of IoT systems through worldwide protection. Most people lack the knowledge or skills necessary to secure devices on their own due to the pervasiveness of IoT. In the context of the IoT, ML is quite effective in resolving safety issues. This study reviews an IoT secure data transmission model for ML-based data security models in coordination with blockchain. This study demonstrates that the levels of secure data transmission are higher than those of the current models when compared to the traditional ones.