Cyber Security within Smart Cities: A Comprehensive Study and a Novel Intrusion Detection-Based Approach.

The expansion of smart cities, facilitated by digital communications, has resulted in an enhancement of the quality of life and satisfaction among residents. The Internet of Things (IoT) continually generates vast amounts of data, which is subsequently analyzed to offer services to residents. The growth and development of IoT have given rise to a new paradigm. A smart city possesses the ability to consistently monitor and utilize the physical environment, providing intelligent services such as energy, transportation, healthcare, and entertainment for both residents and visitors. Research on the security and privacy of smart cities is increasingly prevalent. These studies highlight the cybersecurity risks and the challenges faced by smart city infrastructure in handling and managing personal data. To effectively uphold individuals’ security and privacy, developers of smart cities must earn the trust of the public. In this article, we delve into the realms of privacy and security within smart city applications. Our comprehensive study commences by introducing architecture and various applications tailored to smart cities. Then, concerns surrounding security and privacy within these applications are thoroughly explored subsequently. Following that, we delve into several research endeavors dedicated to addressing security and privacy issues within smart city applications. Finally, we emphasize our methodology and present a case study illustrating privacy and security in smart city contexts. Our proposal consists of defining an Artificial Intelligence (AI) based framework that allows: Thoroughly documenting penetration attempts and cyberattacks; promptly detecting any deviations from security standards; monitoring malicious behaviors and accurately tracing their sources; and establishing strong controls to effectively repel and prevent such threats. Experimental results using the Edge-IIoTset (Edge Industrial Internet of Things Security Evaluation Test) dataset demonstrated good accuracy. They were compared to related state-of-the-art works, which highlight the relevance of our proposal. [ABSTRACT FROM AUTHOR]