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.
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Gore, S., Deshpande, A.S., Mahankale, N., Singha, S., Lokhande, D.B. (2023). A Machine Learning-Based Detection of IoT Cyberattacks in Smart City Application. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. ICT4SD 2023. Lecture Notes in Networks and Systems, vol 782. Springer, Singapore. https://doi.org/10.1007/978-981-99-6568-7_8
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