Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities

The smart city emerged as a model with the rapid growth of robust information and communication technology and the development of ubiquitous sensing technology. A smart city offers enhanced social facilities, transport and accessibility while promoting sustainability by using different sensors to gather data from the surroundings. The data collected can then be used to control urban infrastructure, such as traffic congestion, water supply, environmental monitoring, food services, and more. The smart city can track people’s actions and deliver intelligent travel, intelligent healthcare, entertainment, and other services. Dynamic data change includes intelligent and systems solutions for the functioning of these networks to ensure confusion about events in smart cities. Recent advances in machine learning and artificial information allow intelligent cities to effectively deliver services through a reduction in resource consumption. Cloud-based machine learning models enable resource-restricted devices to interconnect and optimize efficiency. The emerging data collection and device designs are targeted at reducing energy savings rather than risks to privacy and security. Thus, the security and privacy concerns remain as intelligent city networks not only collect information from heterogeneous nodes which are the weakest link and susceptible to cyber-attack. In this chapter, we address security issues in smart city applications; and corresponding countermeasures using artificial intelligence and machine learning. Some attempts to address these protection and privacy problems are then presented for smart health, transport, and smart energy.