{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T13:05:55Z","timestamp":1784293555820,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qinghai University","award":["KFKT-25LAB-07"],"award-info":[{"award-number":["KFKT-25LAB-07"]}]},{"name":"Qinghai University","award":["F2025203071"],"award-info":[{"award-number":["F2025203071"]}]},{"name":"Hebei Natural Science Foundation Committee","award":["KFKT-25LAB-07"],"award-info":[{"award-number":["KFKT-25LAB-07"]}]},{"name":"Hebei Natural Science Foundation Committee","award":["F2025203071"],"award-info":[{"award-number":["F2025203071"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This study presents a novel spatio-temporal detection framework for identifying False Data Injection (FDI) attacks in DC microgrid systems from the perspective of cyber\u2013physical symmetry. While modern DC microgrids benefit from increasingly sophisticated cyber\u2013physical symmetry network integration, this interconnected architecture simultaneously introduces significant cybersecurity vulnerabilities. Notably, FDI attacks can effectively bypass conventional Chi-square detector-based protection mechanisms through malicious manipulation of communication layer data. To address this critical security challenge, we propose a hybrid deep learning framework that synergistically combines: Convolutional Neural Networks (CNN) for robust spatial feature extraction from power system measurements; Long Short-Term Memory (LSTM) networks for capturing complex temporal dependencies; and an attention mechanism that dynamically weights the most discriminative features. The framework operates through a hierarchical feature extraction process: First-level spatial analysis identifies local measurement patterns; second-level temporal analysis detects sequential anomalies; attention-based feature refinement focuses on the most attack-relevant signatures. Comprehensive simulation studies demonstrate the superior performance of our CNN-LSTM-Attention framework compared to conventional detection approaches (CNN-SVM and MLP), with significant improvements across all key metrics. Namely, the accuracy, precision, F1-score, and recall could be improved by at least 7.17%, 6.59%, 2.72% and 6.55%.<\/jats:p>","DOI":"10.3390\/sym17071140","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T15:48:22Z","timestamp":1752680902000},"page":"1140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Data-Driven Attack Detection Mechanism Against False Data Injection Attacks in DC Microgrids Using CNN-LSTM-Attention"],"prefix":"10.3390","volume":"17","author":[{"given":"Chunxiu","family":"Li","sequence":"first","affiliation":[{"name":"School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2380-1639","authenticated-orcid":false,"given":"Xinyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China"},{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaotao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiming","family":"Han","sequence":"additional","affiliation":[{"name":"School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingye","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ijepes.2018.05.003","article-title":"Modified adaptive differential evolution based optimal operation and security of AC-DC microgrid systems","volume":"103","author":"Bharothu","year":"2018","journal-title":"Int. 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