DLACB: Deep Learning Based Access Control Using Blockchain

TL;DR

This paper introduces DLACB, a novel framework combining deep learning and blockchain to enhance access control by authenticating users and logging requests, addressing issues of transparency, security, and privacy in centralized systems.

Contribution

It presents a new distributed access control framework that integrates deep learning with blockchain to improve security and reliability over traditional methods.

Findings

Framework operates correctly in all tested scenarios.

Logs access requests on blockchain for traceability.

Authenticates users effectively using deep learning.

Abstract

In general, deep learning models use to make informed decisions immensely. Developed models are mainly based on centralized servers, which face several issues, including transparency, traceability, reliability, security, and privacy. In this research, we identify a research gap in a distributed nature-based access control that can solve those issues. The innovative technology blockchain could fill this gap and provide a robust solution. Blockchain's immutable and distributed nature designs a useful framework in various domains such as medicine, finance, and government, which can also provide access control as opposed to centralized methods that rely on trusted third parties to access the resources. In existing frameworks, a traditional access control approach is developed using blockchain, which depends on predefined policies and permissions that are not reliable. In this research, we propose DLACB: Deep Learning Based Access Control Using Blockchain, which utilizes a deep learning access control mechanism to determine a user's permissions on a given resource. This proposed framework authenticates the users and logs the access requests on the blockchain to recognize malicious users. The results show that this proposed framework operates correctly for all possible scenarios.