Mustansiriyah Journal of Pure and Applied Sciences

Security surveillance systems based on deep learning and Blockchain techniques: a review

Maysam Majid Sabri

Computer Techniques Enginnering

Haider Kadhim Hoommod

Khalid Ali Hussein

DOI: https://doi.org/10.47831/mjpas.v3i3.326

Keywords: Blockchain, CNN, deep learning, monitoring system, AI

---

Abstract

A building security surveillance system typically refers to a comprehensive setup designed to monitor and enhance security within a specific building.  This system typically integrates various technologies and components to detect, assess, and respond to potential security threats.  The ability of deep learning systems to draw informed conclusions has made them very popular in the field of security monitoring systems.   However, centralized servers in many current deep learning systems prevent providing essential features such as verified data provenance, operational transparency, traceability, and reliability. On the other side, blockchain technology is a distributed and decentralized digital system composed of a series of blocks that include encrypted transaction data that can be shared among network users. Therefore, the integration of deep learning and blockchain technologies into security surveillance systems is an important area that provides improvements in data analysis, privacy, security, and overall efficiency of surveillance systems. This article reviews the significance of integrating deep learning algorithms and blockchain technology to develop a building security monitoring system. Furthermore, research related to integrating deep learning techniques with blockchain technology will be presented. Therefore, topics such as deep convolutional neural networks, blockchain concepts, and the measurements used to link these two technologies will be investigated and discussed.    Finally, we present a comprehensive discussion of the state-of-the-art articles that must be investigated for building a robust deep learning system based on blockchain technology for security monitoring systems.