Dynamic Access Control Policy based on Blockchain and Machine Learning for the Internet of Things

TLDR

The Internet of Things blurs the line between physical and digital worlds, but security and privacy concerns—especially for devices holding intimate data or safeguarding lives—threaten its widespread adoption. This study proposes a dynamic, fully distributed access‑control policy for IoT environments. The policy combines blockchain technology to enforce distribution and reinforcement‑learning algorithms to adaptively optimize security decisions.

Abstract

The Internet of Things (IoT) is now destroying the barriers between the real and digital worlds. However, one of the huge problems that can slow down the development of this global wave, or even stop it, concerns security and privacy requirements. The criticality of these latter comes especially from the fact that the smart objects may contain very intimate information or even may be responsible for protecting people’s lives. In this paper, the focus is on access control in the IoT context by proposing a dynamic and fully distributed security policy. Our proposal will be based, on one hand, on the concept of the blockchain to ensure the distributed aspect strongly recommended in the IoT; and on the other hand on machine learning algorithms, particularly on reinforcement learning category, in order to provide a dynamic, optimized and self-adjusted security policy.