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Title	Smart Infrastructure Design: Machine Learning Solutions For Securing Modern Cities
ID_Doc	51074
Authors	Shuhan W.; Chengzhi Y.; Xiaoxiao L.; Siyu W.
Year	2024
Published	Sustainable Cities and Society, 107
DOI	http://dx.doi.org/10.1016/j.scs.2024.105439
Abstract	In the realm of securing smart cities against emerging cyber threats, this research encompasses three distinct yet interconnected initiatives. First, a pioneering data architecture design leveraging CycleGAN is proposed to counteract False Data Injection Attacks (FDIA). By cyclically transforming data distributions, CycleGAN ensures the integrity and reliability of smart city information, fortifying against malicious manipulations. Second, an innovative Internet of Things (IoT) concept is introduced, aiming to enhance real-time monitoring and context-aware threat detection within Intrusion Detection Systems (IDS). Harnessing the wealth of data generated by IoT devices, this concept provides a comprehensive understanding of network activities, fostering adaptive responses to emerging threats. Lastly, the research delves into the development of a novel IDS hyperparameter adjusting system. This system integrates the strengths of Biogeography-Based Optimization (BBO) and Whale Optimization Algorithm (WOA) to fine-tune IDS configuration parameters. Drawing inspiration from biogeographical principles and collaborative whale behavior, the hybrid optimization system balances exploration and exploitation, adapting the IDS to diverse network environments. Together, these initiatives represent a holistic approach to fortifying smart cities through cutting-edge data architecture, IoT-driven threat detection, and optimized IDS configurations, contributing to the resilience and cybersecurity of modern urban landscapes. © 2024
Author Keywords	CycleGAN; False data injection attacks (FDIA); Internet of things (IOT); Intrusion Detection Systems (IDS); Smart city cybersecurity

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