Weighted Heterogeneous Ensemble for the Classification of Intrusion Detection Using ant Colony Optimization for Continuous Search Spaces

Weighted Heterogeneous Ensemble for the Classification of Intrusion Detection Using ant Colony Optimization for Continuous Search Spaces | 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 Weighted Heterogeneous Ensemble for the Classification of Intrusion Detection Using ant Colony Optimization for Continuous Search Spaces Abdulla Aburomman, Dheeb Albashish This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-805019/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This paper proposes a heterogeneous ensemble classifier configuration for a multiclass intrusion detection problem. The ensemble is composed of k-Nearest Neighbors (kNN), Artificial Neural Networks (ANN), and Naive Bayes (NB) classifiers. The decisions of these classifiers are combined with Weighted Majority Voting (WMV), where optimal weights are generated by Ant Colony Optimization for continuous search spaces (ACOR). As a comparison basis, we have also implemented the ensemble configuration with the unweighted majority voting or Winner Takes All (WTA) strategy. To ensure the maximum variety of classifiers, we have implemented three versions of each classification algorithm by varying each classifier's parameters making a total of nine diverse experts for the ensemble. For our empirical study, we used the full NSL-KDD dataset to classify network traffic into one of five different classes. Our results indicate that the ensemble configuration using ACOR-optimized weights are capable of resolving the conflicts between multiple classifiers and improving the overall classification accuracy of the ensemble. Heterogeneous ensemble Weighted Majority Voting Nearest Neighbor Artificial Neural Networks Naive Bayes Ant Colony Optimization for continuous search spaces Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Mar, 2022 Reviewers invited by journal 10 Mar, 2022 Editor assigned by journal 13 Aug, 2021 First submitted to journal 11 Aug, 2021 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-805019","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":89726084,"identity":"ea2e4508-18f0-440f-9351-63d3adc3bc0c","order_by":0,"name":"Abdulla Aburomman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYDADfgkwJSFDvBbJGQyMDUAtPMRrMbgB1sJAWIt5e/OxDx9z7PKMbzcff3SjxoKHgf3w0Q34tMicOZY8c+a25GKzO8cSm3OOAR3Gk5Z2A58WCYkcY2bebcyJ227kGDbnsAG1SPCY4dci/wakpT5x8wyQln/EaJHgAWk5nLhBAqglt40YLTxpyYwztx1PnHEjLXF2bp8EDxtBv7AfPszwcVt1Yv+M5AOfc77VyfGzHz6GVwsmYCNN+SgYBaNgFIwCbAAAEIpD9Uc/XSIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8420-7604","institution":"UKM: Universiti Kebangsaan Malaysia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Abdulla","middleName":"","lastName":"Aburomman","suffix":""},{"id":89726085,"identity":"8ff15575-fda5-413a-b554-be48269c328b","order_by":1,"name":"Dheeb Albashish","email":"","orcid":"","institution":"Al-Balqa' Applied University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dheeb","middleName":"","lastName":"Albashish","suffix":""}],"badges":[],"createdAt":"2021-08-12 00:36:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-805019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-805019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19138387,"identity":"d923a334-fe87-470e-8b51-a3dbd8b3c937","added_by":"auto","created_at":"2022-03-11 16:54:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1281505,"visible":true,"origin":"","legend":"","description":"","filename":"softcomputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-805019/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eWeighted Heterogeneous Ensemble for the Classification of Intrusion Detection Using ant Colony Optimization for Continuous Search Spaces\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-805019/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Heterogeneous ensemble, Weighted Majority Voting, Nearest Neighbor, Artificial Neural Networks, Naive Bayes, Ant Colony Optimization for continuous search spaces","lastPublishedDoi":"10.21203/rs.3.rs-805019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-805019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper proposes a heterogeneous ensemble classifier configuration for a multiclass intrusion detection problem. 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