Mitigating IoT data management security concerns through blockchain and machine learning based solutions

Abstract

The Internet of Things industry is expanding rapidly. However, many security breaches occur, and privacy is often at stake in traditional IoT networks. These centralized systems will not be able to cope with dynamically changing environments. In light of these risks, it is crucial to prevent and minimize the chances of attacks occurring. Researchers have attempted to use blockchain for IoT security to ensure data consistency and availability. Fully public decentralized solutions for IoT still face data breaches. On the other hand, machine learning models detect potential attacks to create an effective defense system. This paper surveys state-of-the-art works looking to integrate blockchain with machine learning to protect data management for the IoT. Before exploring the various implementations, an analysis of multiple surveys that dive deeper into such integrations is made; then, five different blockchain and machine learning integrations. Many papers need a complete security analysis, and the experiments are limited. From studying the relevant integrations, this article presents a new scheme to protect IoT data using the DQNSB consensus algorithm to train a global model by distributing machine learning tasks, leveraging transparency to guarantee security.