  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.
