Modeling and Dynamic Control for Malware Spread in Software Defined Network

Modeling and Dynamic Control for Malware Spread in Software Defined Network | 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 Modeling and Dynamic Control for Malware Spread in Software Defined Network Yanshang Yin, Yiyao Zhu, Ligang Dong, Xian Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6248928/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 17 You are reading this latest preprint version Abstract Epidemic models were used to study the spread of malware in software defined network (SDN). However, these models cannot efficiently prevent the spread of malware in SDN. Previous studies have not assessed network security and are unable to demonstrate the changing trends in network security. This study considers the features of SDN and the spread delay of malware, innovatively applies methods of isolating infected devices to SDN, and proposes a new SDN-oriented malware spread model. Additionally, a device state transition model describing device connectivity is proposed. Furthermore, considering the isolation and repair costs of SDN controllers, we propose an optimal control algorithm called DDPG-Q, which is based on deep reinforcement learning. DDPG-Q achieves dynamic control of malware spread and reduces repair costs in SDN. Finally, we define network unsafety entropy to describe network security and conduct numerical simulation experiments to confirm the effectiveness of the transition model and DDPG-Q. The results demonstrate an average reduction of approximately 17% in network unsafety entropy. This study establishes a foundation for the dynamic control of malware spread in SDN. \nocite{*} Epidemic models Malware spread Network security Software defined network Unsafety entropy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 31 Mar, 2026 Reviews received at journal 30 Mar, 2026 Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 01 Oct, 2025 Reviews received at journal 08 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers invited by journal 05 May, 2025 Editor assigned by journal 26 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 17 Mar, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6248928","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452243955,"identity":"402224ed-4359-497d-94e5-f37970327d0d","order_by":0,"name":"Yanshang Yin","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yanshang","middleName":"","lastName":"Yin","suffix":""},{"id":452243956,"identity":"751df89b-d2d2-4c7a-b9ab-4c46fdaee02c","order_by":1,"name":"Yiyao Zhu","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Yiyao","middleName":"","lastName":"Zhu","suffix":""},{"id":452243957,"identity":"43973cce-f368-418e-bfb1-c194ec13e7d8","order_by":2,"name":"Ligang Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDCCA0D8gMGGgbEBzGUmUksCQxrpWg7DuERo4Tvee/hFwq/z9sz9y59JMFRYJzawnz2AV4vkmXNpFol9t5kZZ7wxk2A4k57YwJOXgFeLwY0cM4PEnttsjDPOsEkwth1ObJDgMSBGyzkexhnHn0kw/iNOi/GDhB8HJBj7G8wkGBuI0CJ55owZQ2JDsgHjDB5ji4Rj6cZtPDn4tfAd7zH+8OGPnb1h//GHNz7UWMv2s5/BrwUIQL5mYDCckQCKICCXkHogYP7A8IeBQZ7/ABFqR8EoGAWjYEQCAI+RSx8BAoBnAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":true,"prefix":"","firstName":"Ligang","middleName":"","lastName":"Dong","suffix":""},{"id":452243958,"identity":"62308a39-054e-499f-8b06-4a9724addfcf","order_by":3,"name":"Xian Jiang","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-03-18 03:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6248928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6248928/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling and Dynamic Control for Malware Spread in Software Defined Network","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":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Epidemic models, Malware spread, Network security, Software defined network, Unsafety entropy","lastPublishedDoi":"10.21203/rs.3.rs-6248928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6248928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpidemic models were used to study the spread of malware in software defined network (SDN). However, these models cannot efficiently prevent the spread of malware in SDN. Previous studies have not assessed network security and are unable to demonstrate the changing trends in network security. This study considers the features of SDN and the spread delay of malware, innovatively applies methods of isolating infected devices to SDN, and proposes a new SDN-oriented malware spread model. Additionally, a device state transition model describing device connectivity is proposed. Furthermore, considering the isolation and repair costs of SDN controllers, we propose an optimal control algorithm called DDPG-Q, which is based on deep reinforcement learning. DDPG-Q achieves dynamic control of malware spread and reduces repair costs in SDN. Finally, we define network unsafety entropy to describe network security and conduct numerical simulation experiments to confirm the effectiveness of the transition model and DDPG-Q. The results demonstrate an average reduction of approximately 17% in network unsafety entropy. This study establishes a foundation for the dynamic control of malware spread in SDN. \\nocite{*}\u003c/p\u003e","manuscriptTitle":"Modeling and Dynamic Control for Malware Spread in Software Defined Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 18:08:57","doi":"10.21203/rs.3.rs-6248928/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T20:42:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T17:26:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T09:06:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T12:12:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317507657273797985610484652990549843478","date":"2026-03-09T11:28:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101391719047128644018008052668868384715","date":"2026-03-09T09:54:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111860016729653944381292207923432795184","date":"2026-03-08T07:41:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121908447796713302921146213808446133359","date":"2026-03-06T14:25:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146564562914333052712763187533029596551","date":"2025-10-01T06:40:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-08T14:40:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60514593878178473903467727846243903738","date":"2025-06-03T01:45:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111504916033995412048821881435991834217","date":"2025-06-02T05:40:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321421528598069308672489069928149695733","date":"2025-05-05T18:07:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-05T05:30:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-26T09:46:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T02:28:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cluster Computing","date":"2025-03-18T03:12:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"22841293-96a4-4a9c-9ba3-455e56d104b0","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T20:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 18:08:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6248928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6248928","identity":"rs-6248928","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}