An In-Depth Review of Machine Learning, Blockchain, and Deep Learning Models for Intelligent Security and Resilience Enhancement in Smart Cities

A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of machine learning, blockchain, and deep learning models in strengthening security and making the urban environment at smart cities more resilient. Not all the review articles heretofore successfully integrated these three pivotal domains, which lowered their practical applicability and insight depth. This paper reviews recent state-of-the-art models, including supervised and unsupervised machine learning algorithms, blockchain frameworks, and deep advanced learning architectures. The important machine learning models reviewed include Random Forest, Support Vector Machines, and K-means clustering. These were chosen for their already proved effectiveness in anomaly detection, predictive analytics, and classification tasks. On the other hand, blockchain models of Ethereum and Hyperledger Fabric would be evaluated for decentralized security features, immutability, and capability to improve data integrity and transparency. Deep learning models can handle large-scaled, unstructured data and extract complex patterns. Their integration, therefore, shows great promise in enhancing the security and resilience of smart cities. This paper contributes to a holistic, multidisciplinary viewpoint, filling literature gaps by providing a basic framework for further research and implementation of smart city initiatives. Such insights synthesized across these domains set a course for innovation in the formulation of solutions that fortify urban infrastructures against emerging threats and enhance overall urban resilience. (Abstract)