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Title	A Machine Learning-Based Detection Of Iot Cyberattacks In Smart City Application
ID_Doc	2481
Authors	Gore S.; Deshpande A.S.; Mahankale N.; Singha S.; Lokhande D.B.
Year	2023
Published	Lecture Notes in Networks and Systems, 782 LNNS
DOI	http://dx.doi.org/10.1007/978-981-99-6568-7_8
Abstract	The creation of smart cities using IoT-enabled technology has resulted in considerable improvements in operational efficiency and service quality. However, as smart city networks expand, the possibility of cybersecurity attacks and threats increases, which can cause IoT devices to malfunction and potentially threaten people's lives. This study investigates the use of various machine learning algorithms like LR, DT, SVM, RF, ANN, and KNN in smart city networks to sense assaults and anomalies. Ensemble approaches like boosting, bagging, as well as stacking are also used to improve the detection system’s effectiveness. The suggested technique effectively identifies cyberattacks, according to experimental data, and the ensemble technique like stacking beats previous models with enhanced accuracy as well as other performance metrics. This study emphasizes the potential of employing machine learning algorithms and ensemble approaches to identify cyberattacks in IoT-based smart cities, as well as the necessity of the selection of relevant features with cross-validation techniques for increased accuracy and performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Cyberattacks; Ensemble model; IoT; Machine learning; Network security; Smart City

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