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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.<\/jats:p>","DOI":"10.1007\/s00500-022-06761-1","type":"journal-article","created":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T10:03:06Z","timestamp":1642759386000},"page":"13109-13118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["On the performance metrics for cyber-physical attack detection in smart grid"],"prefix":"10.1007","volume":"26","author":[{"given":"Sayawu Yakubu","family":"Diaba","sequence":"first","affiliation":[]},{"given":"Miadreza","family":"Shafie-khah","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"Elmusrati","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"6761_CR1","doi-asserted-by":"publisher","first-page":"92444","DOI":"10.1109\/ACCESS.2019.2927484","volume":"7","author":"LF C\u00f3mbita","year":"2019","unstructured":"C\u00f3mbita LF, C\u00e1rdenas \u00c1A, Quijano N (2019) Mitigating sensor attacks against industrial control systems. 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