Hybrid Firefly and Particle Swarm Optimization for parameter tuning of XGBoost: Network Intrusion Detection

Hybrid Firefly and Particle Swarm Optimization for parameter tuning of XGBoost: Network Intrusion Detection | 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 Hybrid Firefly and Particle Swarm Optimization for parameter tuning of XGBoost: Network Intrusion Detection Paul Mensah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7109054/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 This paper presents an intrusion detection framework combining machine learning, feature engineering, and metaheuristic optimization to enhance network security. The approach employs Extreme Gradient Boosting (XGBoost) for automatic feature selection and model evaluation using confusion matrix-based metrics, including accuracy, precision, recall, F1- score, true positive, and false positive rates. To optimize hyperparameters in high-dimensional datasets, a novel hybrid Firefly Algorithm–Particle Swarm Optimization (FA–PSO) is introduced, improving the exploration of optimal configurations. The framework incorporates comprehensive data preprocessing, feature engineering, and a stacked ensemble learning strategy for improved detection accuracy and generalization. Evaluations on the NSL-KDD dataset demonstrate superior performance over traditional models, confirming the effectiveness of integrating feature engineering with hybrid optimization for scalable, real-time intrusion detection. Intrusion Detection XGBoost Feature Engineering Network Security Machine Learning Anomaly Detection Full Text Additional Declarations No competing interests reported. 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-7109054","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485057344,"identity":"3d993099-15ed-42b4-b0ba-501382588f9b","order_by":0,"name":"Paul Mensah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYPACCwY2Bh7GA0CWHIh74AFhLRIgLQwgLcZgLQnEaGGAaklsAPHxadFt7z324GebRD4f+9kDBz622aTPDzv8EGiLnZxuA3YtZmfOpRv2tklYtvHkJRyc2ZaWu/F2mgFQS7Kx2QEcWm7kmEnwtkkYsEnwGBzmOXM4d+PsBJCWA4nbcGm5/8ZM8i9Cy/90w9npH/BrucFjJo2wpeJAgrx0DgFbzuSYG8ucA2rhyTE4OKMi2XCDdE7BgQQDPH45fsbs4ZsyGwP59jOGDz4Y2MnLz07f/OFDhZ0cLi1AwIbKNQCrNMCpHIsW+Qa8qkfBKBgFo2AEAgCSbV7a2ypyYgAAAABJRU5ErkJggg==","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Paul","middleName":"","lastName":"Mensah","suffix":""}],"badges":[],"createdAt":"2025-07-12 15:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7109054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7109054/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88634984,"identity":"2c69fc0e-a2d9-4f43-82ef-d3b008d7fda6","added_by":"auto","created_at":"2025-08-08 14:47:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":345583,"visible":true,"origin":"","legend":"","description":"","filename":"mymymy3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7109054/v1_covered_31b682d5-1ccf-46a1-8cde-e6f090c2f1f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Firefly and Particle Swarm Optimization for parameter tuning of XGBoost: Network Intrusion Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"Intrusion Detection, XGBoost, Feature Engineering, Network Security, Machine Learning, Anomaly Detection","lastPublishedDoi":"10.21203/rs.3.rs-7109054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7109054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents an intrusion detection framework combining machine learning, feature engineering, and metaheuristic optimization to enhance network security. 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