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Smart city article details

Title	A Knn-Based Intrusion Detection Model For Smart Cities Security
ID_Doc	2253
Authors	Abdedaime M.; Qafas A.; Jerry M.; Guezzaz A.
Year	2023
Published	Lecture Notes in Networks and Systems, 492
DOI	http://dx.doi.org/10.1007/978-981-19-3679-1_20
Abstract	Currently, information technologies are integrated to acquire, manage, and analyze data circulated within smart cities networks and systems. With the growth of technologies, security issues and privacy have been a significant field to anticipate attacks that infect resources. Therefore, many research works aim to include sophisticated techniques, such as artificial intelligence (AI), to monitor smart cities networks, improve their security and then protect data exchanged within their networks. This paper presents an enhanced approach for Internet of Thing (IoT) security in smart cities using AI techniques. Furthermore, we describe in details several suggested solutions to validate our approach. From experimental study, the proposed model gives robust results in terms of 98.4% accuracy (ACC), 96.1% detection rate (DR), and 2.9% false alarms (FAR). The obtained results prove that our approach makes accurate decisions compared with other models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	AI; Classification; IoT; Machine learning; Security; Smart cities

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