Advanced Machine Learning Models with Metaheuristic Enhancements for Active IoT-SDN Network Traffic Analysis

Advanced Machine Learning Models with Metaheuristic Enhancements for Active IoT-SDN Network Traffic Analysis | 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 Advanced Machine Learning Models with Metaheuristic Enhancements for Active IoT-SDN Network Traffic Analysis Nidhi Bajpai, Madhavi Dhingra, Nisha Chaurasia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7445437/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Due to constant connectivity these days, it’s very important to sort and arrange the different types of network traffic well to maintain the best possible performance and computing power in IoT systems. Even so, existing ways to classify traffic in SDN may deal with a wide variety of features, poor selection of important features and a lack of accuracy during dynamic and varied IoT scenarios. Therefore, this paper presents an enhanced machine learning-based technique to optimize network traffic classification in SDN-based IoT networks. It uses traffic information from the Dalhousie NIMS Lab IoT 2024 dataset after processing it with the Bag of Words (BoW) model. PCA (Principal Component Analysis) is used to lower the number of features. With the ESO algorithm, the necessary traffic features are chosen after critical traffic is analyzed. Next, these features are improved and organized using mixed machine learning techniques, for example, Relevance Vector Machines with Secretary Bird Optimization (RVMSBO), Hybrid XGBoost enhanced by Hunter Prey Optimization (HXGBHPO) and an Adjusted Boosting-Random Forest Algorithm (ABCRFA). RVMSBO performed better than other reviewed models, with specificity of 0.98582, an accuracy of 0.98231, precision of 0.98571 and an F1-score of 0.98224. The observed results confirm that the given method helps improve both traffic classification performance and the functioning of the IoT-SDN network. Software-Defined Networking (SDN) Machine Learning IoT traffic features feature selection classification Egret Swarm Optimization (ESO) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Dec, 2025 Reviews received at journal 05 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 26 Oct, 2025 Reviewers agreed at journal 26 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor assigned by journal 23 Sep, 2025 Submission checks completed at journal 21 Sep, 2025 First submitted to journal 21 Sep, 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. 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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-7445437","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533394259,"identity":"f2306ef0-bf71-48a6-bfa8-0a6cf96128d4","order_by":0,"name":"Nidhi Bajpai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie2RvWrDMBRGrxHEi6Gr+xZ3kjEY5UG6WAiSqdCpHRroLYF26QPkJbqajCqCdjH1augS0RfwWsjQm6Qd/TMWqrPoQ9yjT0IAgcBfRIAETbxARNBhwVvRvZ2sRJurxUGhYQVY4fNPetK5YxhUslhUO79VcRY7ggQb9fzouGVVXPQp+Xp2g7o2In/i66X4Yapas/K6uKQeBV0iU/0gBFpWkBXJwUbkxpQ7gY0nKPHdSA5TFCew5RaLVsl2tGV2ner6TeQbTy+EppQtt5RDb2lcdf61vTXZ2dJ/7vdqLpul33Wrolf5xSCcvkMfJ8uR8QMKf8J8wnAgEAj8M74Bhzxk1tlZ0rAAAAAASUVORK5CYII=","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Bajpai","suffix":""},{"id":533394260,"identity":"2447d6c5-db85-4aea-a2da-7f23a043ca9a","order_by":1,"name":"Madhavi Dhingra","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Madhavi","middleName":"","lastName":"Dhingra","suffix":""},{"id":533394268,"identity":"0ac9bf78-6fd1-45d0-8d3a-c57282668107","order_by":2,"name":"Nisha Chaurasia","email":"","orcid":"","institution":"Dr. B. 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Software-Defined Networking (SDN), Machine Learning, IoT, traffic features, feature selection, classification, Egret Swarm Optimization (ESO)","lastPublishedDoi":"10.21203/rs.3.rs-7445437/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7445437/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to constant connectivity these days, it\u0026rsquo;s very important to sort and arrange the different types of network traffic well to maintain the best possible performance and computing power in IoT systems. Even so, existing ways to classify traffic in SDN may deal with a wide variety of features, poor selection of important features and a lack of accuracy during dynamic and varied IoT scenarios. Therefore, this paper presents an enhanced machine learning-based technique to optimize network traffic classification in SDN-based IoT networks. It uses traffic information from the Dalhousie NIMS Lab IoT 2024 dataset after processing it with the Bag of Words (BoW) model. PCA (Principal Component Analysis) is used to lower the number of features. With the ESO algorithm, the necessary traffic features are chosen after critical traffic is analyzed. Next, these features are improved and organized using mixed machine learning techniques, for example, Relevance Vector Machines with Secretary Bird Optimization (RVMSBO), Hybrid XGBoost enhanced by Hunter Prey Optimization (HXGBHPO) and an Adjusted Boosting-Random Forest Algorithm (ABCRFA). RVMSBO performed better than other reviewed models, with specificity of 0.98582, an accuracy of 0.98231, precision of 0.98571 and an F1-score of 0.98224. The observed results confirm that the given method helps improve both traffic classification performance and the functioning of the IoT-SDN network.\u003c/p\u003e","manuscriptTitle":"Advanced Machine Learning Models with Metaheuristic Enhancements for Active IoT-SDN Network Traffic Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 14:51:07","doi":"10.21203/rs.3.rs-7445437/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-24T05:40:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T17:57:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T19:16:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T05:16:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16979510252865683982165002994731282962","date":"2025-10-30T05:03:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T09:39:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T13:11:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25116040793041172256045860296006404795","date":"2025-10-26T12:11:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176523728612086806830879597673622885923","date":"2025-10-24T12:18:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165086076613311812243820997282913208903","date":"2025-10-24T12:14:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283885493361896228309768541500039608359","date":"2025-10-24T12:12:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T12:12:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T07:10:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150721038912657423917426564783520273782","date":"2025-10-10T06:33:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244876068463908844338092942605785657956","date":"2025-10-09T14:38:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T14:20:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T12:16:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-21T15:45:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Internet of Things","date":"2025-09-21T15:41:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-internet-of-things","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diot","sideBox":"Learn more about [Discover Internet of Things](https://www.springer.com/journal/43926)","snPcode":"","submissionUrl":"","title":"Discover Internet of Things","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c5c1e3b-dc04-4175-9886-89e070ef1135","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T11:24:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 14:51:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7445437","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7445437","identity":"rs-7445437","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}