A Unified Cross-Domain Framework for DDoS Detection Across IoT, Cloud, and SDN Networks

A Unified Cross-Domain Framework for DDoS Detection Across IoT, Cloud, and SDN Networks | 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 A Unified Cross-Domain Framework for DDoS Detection Across IoT, Cloud, and SDN Networks Muhammad Ali Azam Azam Khattak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8018737/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The convergence of Internet of Things (IoT), cloud computing, and Software-Defined Networking (SDN) has increased the attack surface for large-scale Distributed Denial- of-Service (DDoS) attacks. While machine-learning and deep-learning methods achieve strong results within single domains, they frequently fail to generalize across hetero- geneous network environments. We propose a unified cross-domain DDoS detection framework that harmonizes traffic from IoT, cloud, and SDN domains into a shared, semantically consistent feature space, enabling fair comparison and transferability assessment. Five supervised classifiers Random Forest, XGBoost, LightGBM, Cat- Boost, and a Multi-Layer Perceptronare evaluated under source → target transfer scenarios using standardized metrics and statistical testing. Experiments on repre- sentative IoT, cloud, and SDN benchmark datasets show consistently high detection performance (accuracy ≥ 0.987) and area under the ROC curve (AUC ≥ 0.998) after harmonization, with only minor precision degradation across merged datasets. Feature-importance and interpretability analyses identify flow duration, packet-length statistics, and inter-arrival variance as invariant indicators of volumetric DDoS activ- ity. The results demonstrate the frameworks robustness, scalability, and practical suitability for deployment in next-generation heterogeneous network infrastructures. Theoretical Computer Science DDoS detection cross-domain learning software-defined networking cloud computing IoT security cybersecurity analytics Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Manuscript.zip Cite Share Download PDF Status: Posted Version 1 posted 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-8018737","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539162808,"identity":"0c572b1d-5945-4f2f-acbd-28a81fbc1774","order_by":0,"name":"Muhammad Ali Azam Azam 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Peshawar","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DDoS detection, cross-domain learning, software-defined networking, cloud computing, IoT security, cybersecurity analytics","lastPublishedDoi":"10.21203/rs.3.rs-8018737/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8018737/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe convergence of Internet of Things (IoT), cloud computing, and Software-Defined Networking (SDN) has increased the attack surface for large-scale Distributed Denial- of-Service (DDoS) attacks. While machine-learning and deep-learning methods achieve strong results within single domains, they frequently fail to generalize across hetero- geneous network environments. We propose a unified cross-domain DDoS detection framework that harmonizes traffic from IoT, cloud, and SDN domains into a shared, semantically consistent feature space, enabling fair comparison and transferability assessment. Five supervised classifiers Random Forest, XGBoost, LightGBM, Cat- Boost, and a Multi-Layer Perceptronare evaluated under source\u003cem\u003e\u0026rarr;\u003c/em\u003etarget transfer scenarios using standardized metrics and statistical testing. Experiments on repre- sentative IoT, cloud, and SDN benchmark datasets show consistently high detection performance (accuracy\u0026thinsp;\u003cem\u003e\u0026ge;\u003c/em\u003e\u0026thinsp;0.987) and area under the ROC curve (AUC\u0026thinsp;\u003cem\u003e\u0026ge;\u003c/em\u003e\u0026thinsp;0.998) after harmonization, with only minor precision degradation across merged datasets. Feature-importance and interpretability analyses identify flow duration, packet-length statistics, and inter-arrival variance as invariant indicators of volumetric DDoS activ- ity. The results demonstrate the frameworks robustness, scalability, and practical suitability for deployment in next-generation heterogeneous network infrastructures.\u003c/p\u003e","manuscriptTitle":"A Unified Cross-Domain Framework for DDoS Detection Across IoT, Cloud, and SDN Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 12:11:41","doi":"10.21203/rs.3.rs-8018737/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"82f6c52e-c12b-40ea-ba68-72deec8f6508","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57339419,"name":"Theoretical Computer Science"}],"tags":[],"updatedAt":"2025-11-04T12:11:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 12:11:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8018737","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8018737","identity":"rs-8018737","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}