Machine Learning Based Data Security Model Using Blockchain for Secure Data Transmission in IoT

Abstract

The Internet of Things (IoT) Internet typically provides information on all Internet properties. Without human interference, it monitors and handles the functions remotely. It can immediately or through its experiences adapt to the conditions. Because of the participation of IoT devices in many applications, security and privacy of the users have become a matter of major concern. The current security and privacy policies are being undermined by cyber-attacks at an explosive pace. Blockchain technology recently became one of the most advanced technique in IOT. Blockchain has been developed to facilitate digital transactions and to provide safe and reliable access to the distributed ledger. Blockchain is able to provide safe transactions among users without the need for reliable third parties or intermediaries with intelligent contracts. Each node data transmission packets rate will be recorded using the blockchain. Consequently, Machine Learning (ML) algorithms are employed to produce precise outputs from large and complex databases that enable produced outputs to predict and detect IoT-based systems' vulnerabilities. Data from heterogeneous sensors can be collected by translating the differing types of values for various sensor types. In addition to fast IoT system expansion via global protection, it is not expected to reach its highest level. As a result of the omnipresent existence of IoT, most users have no experience or ability to protect devices alone. In IoT world, machine learning can be extremely successful in addressing safety challenges. In this research work, a Machine Learning based Data Security Model with Blockchain (MLDSMB) for IoT secure data transmission is proposed. The proposed model is contrasted with the traditional models and the proposed technique shows that the secure data transmission levels are high than existing models.