Deep Learning, Blockchain based Multi-layered Authentication and Security Architectures

Because of its ability to make educated judgments, deep learning has gained massive attention in recent years. Many of today's deep learning systems rely on centralized servers and lack operational transparency, traceability, dependability, security, and trustworthy data provenance. Furthermore, using centralized data to train deep learning models exposes them to the single point of failure issue. The relevance of combining blockchain technology and deep learning is highlighted in this study. Deep-learning (DL) techniques are used to authenticate and identify abnormalities and provide security for systems (in cryptographic and biometric systems). Confidentiality and effectiveness must be balanced since network sensors are energy-constrained devices, which is the most crucial idea to consider when establishing a security system that relies on deep-learning techniques and blockchain. Centralized systems have several flaws. Mostly, a network with significant demand for smart devices creates a prodigious amount of data. There is always the possibility that one or more of the centralized network's major components would fail, causing a catastrophic (or full) system failure. The data acquired by the centralized cloud storage frequently necessitates third-party modification. This may result in data breaches, jeopardizing the privacy of the end-user. This paper primarily focuses on the security models proposed in terms of authentication and security using Blockchain, Deep Learning, or the integration of both based on certain characteristics to identify and organize the literature, inclouding type, models, consensus protocols, applications, services, and deployment goals.