{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:51:56Z","timestamp":1787028716010,"version":"3.56.0"},"reference-count":217,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T00:00:00Z","timestamp":1695772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laboratory of Renewable Energies and Advanced Materials (LERMA)"},{"name":"College of Engineering and Architecture of the International University of Rabat (IUR)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>Smart grids have emerged as a transformative technology in the power sector, enabling efficient energy management. However, the increased reliance on digital technologies also exposes smart grids to various cybersecurity threats and attacks. This article provides a comprehensive exploration of cyberattacks and cybersecurity in smart grids, focusing on critical components and applications. It examines various cyberattack types and their implications on smart grids, backed by real-world case studies and quantitative models. To select optimal cybersecurity options, the study proposes a multi-criteria decision-making (MCDM) approach using the analytical hierarchy process (AHP). Additionally, the integration of artificial intelligence (AI) techniques in smart-grid security is examined, highlighting the potential benefits and challenges. Overall, the findings suggest that \u201csecurity effectiveness\u201d holds the highest importance, followed by \u201ccost-effectiveness\u201d, \u201cscalability\u201d, and \u201cIntegration and compatibility\u201d, while other criteria (i.e., \u201cperformance impact\u201d, \u201cmanageability and usability\u201d, \u201ccompliance and regulatory requirements\u201d, \u201cresilience and redundancy\u201d, \u201cvendor support and collaboration\u201d, and \u201cfuture readiness\u201d) contribute to the evaluation but have relatively lower weights. Alternatives such as \u201caccess control and authentication\u201d and \u201csecurity information and event management\u201d with high weighted sums are crucial for enhancing cybersecurity in smart grids, while alternatives such as \u201ccompliance and regulatory requirements\u201d and \u201cencryption\u201d have lower weighted sums but still provide value in their respective criteria. We also find that \u201cdeep learning\u201d emerges as the most effective AI technique for enhancing cybersecurity in smart grids, followed by \u201chybrid approaches\u201d, \u201cBayesian networks\u201d, \u201cswarm intelligence\u201d, and \u201cmachine learning\u201d, while \u201cfuzzy logic\u201d, \u201cnatural language processing\u201d, \u201cexpert systems\u201d, and \u201cgenetic algorithms\u201d exhibit lower effectiveness in addressing smart-grid cybersecurity. The article discusses the benefits and drawbacks of MCDM-AHP, proposes enhancements for its use in smart-grid cybersecurity, and suggests exploring alternative MCDM techniques for evaluating security options in smart grids. The approach aids decision-makers in the smart-grid field to make informed cybersecurity choices and optimize resource allocation.<\/jats:p>","DOI":"10.3390\/jcp3040031","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T08:24:44Z","timestamp":1695889484000},"page":"662-705","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":120,"title":["Cyberattacks in Smart Grids: Challenges and Solving the Multi-Criteria Decision-Making for Cybersecurity Options, Including Ones That Incorporate Artificial Intelligence, Using an Analytical Hierarchy Process"],"prefix":"10.3390","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3613-955X","authenticated-orcid":false,"given":"Ayat-Allah","family":"Bouramdane","sequence":"first","affiliation":[{"name":"Laboratory of Renewable Energies and Advanced Materials (LERMA), College of Engineering and Architecture, International University of Rabat (IUR), IUR Campus, Technopolis Park, Rocade Rabat-Sal\u00e9, Sala Al Jadida 11103, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"ref_1","unstructured":"Bouramdane, A.A. 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