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Title	An Efficient Detection And Prediction Of Intrusion In Smart Grids Using Artificial Neural Networks
ID_Doc	7830
Authors	Kumar S.M.; Velluri R.; Dayananda P.; Nagaraj S.; Srikantaswamy M.; Chandrappa K.Y.
Year	2024
Published	Lecture Notes in Networks and Systems, 922 LNNS
DOI	http://dx.doi.org/10.1007/978-981-97-0975-5_45
Abstract	In recent years, fraud identification on Internet of Things (IoT) devices has been essential to obtaining better results in all fields, such as smart cities, smart grids, etc. As a result, there are more IoT devices in the smart grid's power management sectors, and apart from these identifications, intrusion into the smart grid is very difficult. Hence, to overcome this, a proposed intrusion detection system in a smart grid using an artificial neural network (ANN) has been used to detect the intrusion and improve the prediction rate, and it has been very effective on various faults injected into the smart grids in ranges and seasons. As per the simulation result, the proposed method shows better results as compared to a conventional neural network (CNN) with respect to the root mean square error in terms of weekly, monthly, and seasonal terms of 0.25%, 0.15%, and 0.26%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Author Keywords	Artificial neural network (ANN); Conventional neural network (CNN); Internet of things (IoT); Intrusion detection; Root mean square error (RMSE)

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