A Machine Learning Approach for Blockchain-Based Smart Home Networks Security

Abstract

Realizing secure and private communications on the Internet of Things (IoT) is challenging, primarily due to IoT's projected vast scale and extensive deployment. Recent efforts have explored the use of blockchain in decentralized protection and privacy supported. Such solutions, however, are highly demanding in terms of computation and time requirements, barring these solutions from the majority of IoT applications. Specifically, in this paper, we introduce a resource-efficient, blockchain-based solution for secure and private IoT. The solution is made possible through novel exploitation of computational resources in a typical IoT environment (e.g., smart homes), along with the use of an instance of Deep Extreme Learning Machine (DELM). In this proposed approach, the Smart Home Architecture based in Blockchain is protected by carefully evaluating its reliability in regard to the essential security aims of privacy, integrity, and accessibility. In addition, we present simulation results to emphasize that the overheads created by our method (in terms of distribution, processing time, and energy consumption) are marginal related to their protection and privacy benefits.