Smart City Gnosys

Smart city article details

Title	Evaluating Deep Learning For Detecting Data Integrity Attacks In Energy Smart Grids
ID_Doc	24568
Authors	Younisse R.; AlKasassbeh M.
Year	2025
Published	2025 International Conference on New Trends in Computing Sciences, ICTCS 2025
DOI	http://dx.doi.org/10.1109/ICTCS65341.2025.10989460
Abstract	The quality of energy services tightly conditions the transition to Industry 4.0 and smart cities. Modern smart grid systems are changing the game’s rules by opening the door for green energy integration and enhancing the grid’s connectivity. However, the connectivity implemented in smart grid systems imposes many data security vulnerabilities, including data integrity attacks. This paper explores the ability of various intelligent learning models to detect data integrity attacks in smart grid systems. The models analyzed include Deep Q-Network (DQN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). The dataset is balanced using the SMOTE model to improve the model’s performance. The CNN model shows promising results in detecting integrity attacks in smart grid systems. Moreover, the DQN model shows a limited ability to classify the attacks. The study presents recommendations and directions to enhance data integrity attack detection in smart grid systems. ©2025 IEEE.
Author Keywords	cybersecurity; deep learning; reinforcement learning; Smart grid systems

Similar Articles

Id	Similarity	Authors	Title	Published
19282	0.888	Martinelli F.; Mercaldo F.; Santone A.	Detection Of Smart Grids Instability With Convolutional Neural Networks And Global Explainability	2023 8th International Conference on Smart and Sustainable Technologies, SpliTech 2023 (2023)
10158	0.88	Salam A.; El Hibaoui A.	Applying Deep Learning Model To Predict Smart Grid Stability	Proceedings of 2021 9th International Renewable and Sustainable Energy Conference, IRSEC 2021 (2021)
44345	0.879	Mohammadpourfard M.; Ghanaatpishe F.; Weng Y.; Genc I.; Sandikkaya M.T.	Real-Time Detection Of Cyber-Attacks In Modern Power Grids With Uncertainty Using Deep Learning	SEST 2022 - 5th International Conference on Smart Energy Systems and Technologies (2022)
50975	0.874	Gunduz M.Z.; Das R.	Smart Grid Security: An Effective Hybrid Cnn-Based Approach For Detecting Energy Theft Using Consumption Patterns	Sensors, 24, 4 (2024)
23955	0.871	Rao S.P.C.; Swarnam S.; Kumar N.; Sheikameer Batcha S.; Sivalanka V.; Chakravarthi A.V.D.	Enhancing Smart City Efficiency By Mitigating Electricity Theft In Smart Grids Using Lightweight Dnn And Smo	3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025 (2025)
8003	0.867	Xie R.	An Energy Theft Detection Framework With Privacy Protection For Smart Grid	Proceedings of the International Joint Conference on Neural Networks, 2023-June (2023)
20294	0.865	Tan Z.; Li Z.	Digital Twins For Sustainable Design And Management Of Smart City Buildings And Municipal Infrastructure	Sustainable Energy Technologies and Assessments, 64 (2024)
10561	0.864	Sulaiman A.; Nagu B.; Kaur G.; Karuppaiah P.; Alshahrani H.; Reshan M.S.A.; AlYami S.; Shaikh A.	Artificial Intelligence-Based Secured Power Grid Protocol For Smart City	Sensors, 23, 19 (2023)
14289	0.858	Naeem H.; Ullah F.; Srivastava G.	Classification Of Intrusion Cyber-Attacks In Smart Power Grids Using Deep Ensemble Learning With Metaheuristic-Based Optimization	Expert Systems, 42, 1 (2025)
35144	0.858	Kamilin M.H.B.; Yamaguchi S.; Ahmadon M.A.B.	Leveraging Trusted Input Framework To Correct And Forecast The Electricity Load In Smart City Zones Against Adversarial Attacks	ICFTSS 2024 - International Conference on Future Technologies for Smart Society (2024)