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Title	Ai Enabled Digital Twin Models To Enhance Security In Smart Cities
ID_Doc	6931
Authors	Mayan A.; Krishanveni S.; Jothi B.
Year	2024
Published	2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024
DOI	http://dx.doi.org/10.1109/IC-SIT63503.2024.10862955
Abstract	As smart cities continue to expand, the security of interconnected systems, such as smart grids, water distribution, and waste management, is paramount. Traditional security measures often lack the predictive capabilities needed to identify and counter cyber threats effectively, posing risks to these critical infrastructures. This research investigates the use of AIenabled digital twin models to strengthen security in smart city environmental management systems. Utilizing Autodesk Tandem as the digital twin platform, we simulate these subsystems and integrate a machine learning (ML) model to monitor network fluctuations, simulate cyber-attacks, and evaluate response strategies. Specifically, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, trained on the CICIDS2017 dataset, is applied to detect threats in real time. By observing subsystem behavior under attack conditions, we identify vulnerabilities and develop enhanced preventive measures. This approach seeks to create a robust security framework capable of predicting, detecting, and mitigating cyber threats in smart city infrastructures. © 2024 IEEE.
Author Keywords	AI; CNN-LSTM; Digital Twin; Smart girds; smart waste; smart water

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