Adaptive network security defense method combining multi-level federated learning and adversarial training

Adaptive network security defense method combining multi-level federated learning and adversarial training | 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 Adaptive network security defense method combining multi-level federated learning and adversarial training Yuanyuan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9265069/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract In order to enhance the network security defense capability in distributed environments, this paper proposes an adaptive defense method that combines multi-level federated learning and adversarial training. This method has demonstrated excellent performance in multiple network attack scenarios. In DDoS attack scenarios, its accuracy reaches 96.8% and F1 score is 0.962, which is significantly better than traditional methods. When facing unknown types of attacks such as zero day attacks and new DDoS attacks, the detection rates are 83.7% and 87.6%, respectively. In addition, this method performs outstandingly in terms of communication efficiency, with a single round communication data volume of only 745.3MB for 100 participants, which is 26.1% of the traditional method. The experimental results show that this method effectively reduces communication overhead and system delay while ensuring defense accuracy, and has good robustness and scalability. Multi level Federated Learning Adversarial training Network security defense Robustness Communication Efficiency Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 30 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. 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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-9265069","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621852480,"identity":"41afaaf0-2046-4413-9942-2d7e61a18b45","order_by":0,"name":"Yuanyuan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACgwPIjA8VNjz8/A0kaGGccSZNRnLGAdzKQUASZiRICzNny2Ebg4YE/Fr4JZKfPeZts8szOH728GvGhvM8BgwHGD98zMGthU0izdyYty252OxMXpp14Y7bPObMDcySM7fh05JgJs3bxpy47UCOmfHMM7d5LBsOsDHz4tWS/g2opT5x2/k3ZkDrzvEYHEggpCUHZMvhxP03coyBnjpAhBaeN2WSc84dT9x5440ZMJCTeSRnHGzG7xf29G0Sb8qqEzeczzH+8KHCzp6fv/ngh494tIAAEw/MkRCasQG/epCSHxCa+QNBpaNgFIyCUTAiAQDaJVgGqwlXbQAAAABJRU5ErkJggg==","orcid":"","institution":"Nantong Vocational University","correspondingAuthor":true,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-30 09:42:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9265069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9265069/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107480837,"identity":"835f271f-578b-4ecf-9aff-d7773b7fb219","added_by":"auto","created_at":"2026-04-22 02:13:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":986015,"visible":true,"origin":"","legend":"","description":"","filename":"manu.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9265069/v1_covered_760a9ee8-de51-4034-a85a-d6f4bbfb66a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive network security defense method combining multi-level federated learning and adversarial training","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-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multi level Federated Learning, Adversarial training, Network security defense, Robustness, Communication Efficiency","lastPublishedDoi":"10.21203/rs.3.rs-9265069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9265069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to enhance the network security defense capability in distributed environments, this paper proposes an adaptive defense method that combines multi-level federated learning and adversarial training. 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