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Title	Building An Intelligent Anomaly Detection Model With Ensemble Learning For Iot-Based Smart Cities
ID_Doc	12988
Authors	Hazman C.; Benkirane S.; Guezzaz A.; Azrour M.; Abdedaime M.
Year	2023
Published	Environmental Science and Engineering
DOI	http://dx.doi.org/10.1007/978-3-031-25662-2_23
Abstract	Internet of Things (IoT) solutions are enabling smart cities across the planet. A smart city concept demands the incorporation of communication and information technologies and devices across an infrastructure in to enhance customer services. They are becoming more tempting to attackers due to their rising number and mobility. To safeguard IoT, numerous solutions, such as encryption, authentication, availability, and data integrity, have been coupled. IDSs are a strong security instrument that could be enhanced by adding machine learning (ML) and deep learning (DP) approaches. The present work introduces an anomaly detection, a robust intrusion detection system for IoT-based smart settings that uses Ensemble Learning. Generally, the framework offered an optimum anomaly detection model that combines AdaBoost, as well as incorporating several feature selection approaches Boruta, mutual information, and correlation. The suggested model was tested using the GPU on the IoT-23, BoT-IoT, and Edge-IIoT datasets. After a lightweight comparison with recent IDS, our technique offers excellent rating performance attributes of ACC, recall, and precision, with 99.9% on recording detection and computation times approximately 33.68 s during training and 0.02156 s during detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	BoT-IoT; Edge-IIoT; Ensemble Learning; Intrusion Detection; IoT; IoT-23; ML; Smart environments

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