DP-Protected LightGBM Framework for Smart Home Malicious Traffic Detection

DP-Protected LightGBM Framework for Smart Home Malicious Traffic 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 DP-Protected LightGBM Framework for Smart Home Malicious Traffic Detection Shimei Jin, Yinglai Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8465439/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract With the maturation of IoT technology and the growing demand for improved quality of life, smart home systems are experiencing rapid development. The explosive growth in both the variety and quantity of smart home devices has brought increasingly severe cybersecurity challenges. However, most detection systems currently available on the market suffer from excessively high computational complexity, making them difficult to deploy in practical scenarios. Furthermore, many models lack sufficient security, allowing hackers to infer whether samples participated in model training by analyzing prediction probability distributions and confidence levels, leading to widespread issues such as information leakage. To address these challenges, this paper proposes a lightweight malicious information detection model integrating differential privacy training mechanisms. The model first performs data preprocessing, then applies differential privacy techniques to add noise to the training set, thereby resisting membership inference attacks. Finally, it employs the LightGBM model to detect malicious information within smart home devices. Experimental results demonstrate that even under low privacy budget constraints, the LightGBM model maintains low resource overhead while achieving high accuracy, precision, recall, F1 score, and AUC values. Smart home Intrusion detection Differential privacy LighrGBM model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 28 Dec, 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-8465439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591925181,"identity":"eadc3bf2-3b2f-41b3-be6c-225bc72d2333","order_by":0,"name":"Shimei Jin","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Shimei","middleName":"","lastName":"Jin","suffix":""},{"id":591925182,"identity":"8e6d5e5d-98f2-4bfe-a083-991b1b473b70","order_by":1,"name":"Yinglai Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACg/vnv0nzth2QY2BgfADkMxPWYnGDhw2kxRio2oA4LTYgLTxnDiQ2kKil4kD6dvZmNgmGCuvEBvazB/BqMbvff0z6j8GB3J09h4FazqQnNvDkJeDXArYFqGXDjfxjEoxthxMbJHgM8GoxhmpJN7j/mE2C8R8RWgyhWhIMbjADtTQQp4XZGKjFcMOZZGaLhGPpxm08Ofi1GNw/w/iYx+CfvMHxw4w3PtRYy/azn8GvBRUkADEbCepHwSgYBaNgFOAAAL88RUh3gP1rAAAAAElFTkSuQmCC","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Yinglai","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-12-28 11:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8465439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8465439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963023,"identity":"e6dbc1b5-da97-40ec-b978-bd4886a83ed5","added_by":"auto","created_at":"2026-02-19 04:12:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":858712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8465439/v1_covered_fcd9dbda-0fdf-436e-a394-e26509cd03ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DP-Protected LightGBM Framework for Smart\nHome Malicious Traffic Detection\n\n\n","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":true,"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":"Smart home, Intrusion detection, Differential privacy, LighrGBM model","lastPublishedDoi":"10.21203/rs.3.rs-8465439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8465439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the maturation of IoT technology and the growing demand for improved quality of life, smart home systems are experiencing rapid development. 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