Multi scale attention driven DACDiff+distributed power FDIAs defense model

Multi scale attention driven DACDiff+distributed power FDIAs defense model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi scale attention driven DACDiff+distributed power FDIAs defense model Jie Wang, Chang Liu, Guowei Zhu, Jiangpei Xu, Feng Long This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9239909/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This paper proposes a novel defense model called DACDiff + to address false data injection attacks (FDIAs) in distributed power systems. This model integrates multi-scale attention mechanism and dynamic adaptive diffusion model, which can effectively capture short, medium, and long-term dependencies in time series data and dynamically adjust the denoising step size according to the attack intensity. The experimental results show that DACDiff+improves data recovery accuracy by 32.7% compared to traditional statistical methods, achieves a defense success rate of 86.7% against stealth attacks, and has a inference time of 32.5ms, meeting real-time requirements. The model validated the key role of multi-scale attention mechanism and dynamic diffusion model through ablation experiments, providing an efficient solution for smart grid security. Distributed power system false data injection attacks (FDIAs) multi-scale attention mechanism dynamic adaptive diffusion model DACDiff+ Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 27 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 27 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9239909","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625885732,"identity":"c9edde83-b191-474c-bd58-4279c4b54f60","order_by":0,"name":"Jie Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBAC9mYGNgkgXc/P3nzgwIcfRGhhhGpJkOw5lnhwZg8xWhqgWgxm5Bgf5mAjRks78zGJnztq8wwYcj4cZuBhkOcXO0DIYWxpN3vPHC82Zzi74XCBBYPhzNkJhLTwmN3gbTvGuLOxd8PhGTxAF94mqIX/282/QC0bDvM8OMzDRoQWwWYe9t+8bTWJG47xMBCnRZqZzdxatu2AsWQPmwEwkCUI+4WP//AzybdtdXL88o8ff/jww0aeX5qAFig4DGNIEKUcBOqIVjkKRsEoGAUjEAAAhQNI+nMsWgYAAAAASUVORK5CYII=","orcid":"","institution":"State Grid Hubei Electric Power Research Institute, Hubei Wuhan","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wang","suffix":""},{"id":625885733,"identity":"d126174b-847d-4510-b23e-ed3e9d1d1c83","order_by":1,"name":"Chang Liu","email":"","orcid":"","institution":"State Grid Hubei Electric Power Research Institute, Hubei Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Liu","suffix":""},{"id":625885734,"identity":"b5915ebc-59ae-44a6-b604-3bf7695e828c","order_by":2,"name":"Guowei Zhu","email":"","orcid":"","institution":"State Grid Hubei Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Guowei","middleName":"","lastName":"Zhu","suffix":""},{"id":625885735,"identity":"9cbc0138-efd7-48da-92cc-c9ac944ea268","order_by":3,"name":"Jiangpei Xu","email":"","orcid":"","institution":"State Grid Hubei Electric Power Research Institute, Hubei Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Jiangpei","middleName":"","lastName":"Xu","suffix":""},{"id":625885736,"identity":"df182488-ecd0-477f-9036-590c04bfe226","order_by":4,"name":"Feng Long","email":"","orcid":"","institution":"State Grid Hubei Electric Power Research Institute, Hubei Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2026-03-27 04:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9239909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9239909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707987,"identity":"53a53c4d-3f66-410b-a219-accb17df4a4e","added_by":"auto","created_at":"2026-04-24 09:21:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1504812,"visible":true,"origin":"","legend":"","description":"","filename":"article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9239909/v1_covered_bc518bca-3407-497f-9f4d-2a708eff494d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi scale attention driven DACDiff+distributed power FDIAs defense model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Distributed power system, false data injection attacks (FDIAs), multi-scale attention mechanism, dynamic adaptive diffusion model DACDiff+","lastPublishedDoi":"10.21203/rs.3.rs-9239909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9239909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes a novel defense model called DACDiff\u0026thinsp;+\u0026thinsp;to address false data injection attacks (FDIAs) in distributed power systems. This model integrates multi-scale attention mechanism and dynamic adaptive diffusion model, which can effectively capture short, medium, and long-term dependencies in time series data and dynamically adjust the denoising step size according to the attack intensity. The experimental results show that DACDiff+improves data recovery accuracy by 32.7% compared to traditional statistical methods, achieves a defense success rate of 86.7% against stealth attacks, and has a inference time of 32.5ms, meeting real-time requirements. 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