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- Title
On the performance metrics for cyber-physical attack detection in smart grid.
- Authors
Diaba, Sayawu Yakubu; Shafie-khah, Miadreza; Elmusrati, Mohammed
- Abstract
Supervisory Control and Data Acquisition (SCADA) systems play an important role in Smart Grid. Though the rapid evolution provides numerous advantages it is one of the most desired targets for malicious attackers. So far security measures deployed for SCADA systems detect cyber-attacks, however, the performance metrics are not up to the mark. In this paper, we have deployed an intrusion detection system to detect cyber-physical attacks in the SCADA system concatenating the Convolutional Neural Network and Gated Recurrent Unit as a collective approach. Extensive experiments are conducted using a benchmark dataset to validate the performance of the proposed intrusion detection model in a smart metering environment. Parameters such as accuracy, precision, and false-positive rate are compared with existing deep learning models. The proposed concatenated approach attains 98.84% detection accuracy which is much better than existing techniques.
- Subjects
CYBER physical systems; SUPERVISORY control &; data acquisition systems; CONVOLUTIONAL neural networks; SUPERVISORY control systems; RECURRENT neural networks; DEEP learning; MACHINE-to-machine communications
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2022, Vol 26, Issue 23, p13109
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-022-06761-1