Network traffic prediction model based on WT-TBiLSTM

Network traffic prediction model based on WT-TBiLSTM | 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 Article Network traffic prediction model based on WT-TBiLSTM Yu Yang, Jinliang Yuan, Minna Gao, Rong Zhao, Fang Shen, Mingqi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6803229/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 Accurate short-term network traffic prediction is critical for proactive network security management, yet remains challenging due to the inherent complexity, noise, and non-stationarity of traffic data. To address this, we propose WT-TBiLSTM, a hybrid deep learning model integrating Wavelet Transform (WT) for noise reduction and a Topology-Attention-enhanced Bidirectional LSTM (TBiLSTM) for temporal feature extraction. First, the WT module decomposes raw traffic data using Daubechies8 wavelets, applies adaptive soft-thresholding to suppress noise, and reconstructs denoised signals through multi-scale analysis. The TBiLSTM module then captures bidirectional temporal dependencies while leveraging a topology attention mechanism to emphasize local structural patterns, enhancing sensitivity to abrupt traffic variations. Experiments on two real-world datasets (UK\_Academic and EU\_Core) demonstrate the model’s superiority. Compared to baseline models , WT-TBiLSTM reduces prediction errors by 64.37% RMSE, 65.45% MAE, and 67.89% MAPE on UK\_Academic, and by 37.26% RMSE, 39.35% MAE, and 38.76% MAPE on EU\_Co. The integration of wavelet denoising and topology attention significantly improves robustness against noise and captures multi-scale temporal dynamics, enabling precise short-term predictions. This work provides a practical solution for real-time network traffic forecasting, empowering administrators to preemptively mitigate security risks and optimize resource allocation. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computer science WT-TBiLSTM Adaptive soft-thresholding Topology attention Network traffic prediction Full Text Additional Declarations No competing interests reported. Supplementary Files code.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-6803229","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":543088030,"identity":"07a2999a-b4f1-4532-a541-35ba6c0afc20","order_by":0,"name":"Yu Yang","email":"","orcid":"","institution":"Chinese People’s Armed Police Force Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Yang","suffix":""},{"id":543088033,"identity":"edf73194-8f49-4169-a5ae-4b624b7ce54f","order_by":1,"name":"Jinliang Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACPmYGNhib8UFCRQ1hLWwgLQcgbGaDB2eOEaGFAaGFTfJhCzMRWtjZrz3+UHEvccPxs8cqEhvYGPjbuxMIOIyn3ODAmeLEDWfy0m4k7pBhkDhzdgMhLWkSB9sSEjccyDG7kXiGjcFAIpcYLf+AWs6/MStIbGMmRgv7MYmDDUAtN3LMGIjUwsMmceZYgvHMG2+MJRLOHOMh6Bd+/uPPJCpqEmT7zucYfvxRUSPH396LXwsDA48BiHRsgHEJKAcB9gcg0p4IlaNgFIyCUTBSAQB3u0nvl+1Y5wAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese People’s Armed Police Force Engineering University","correspondingAuthor":true,"prefix":"","firstName":"Jinliang","middleName":"","lastName":"Yuan","suffix":""},{"id":543088037,"identity":"da8c4ed9-356a-4737-96aa-acce9e210e09","order_by":2,"name":"Minna Gao","email":"","orcid":"","institution":"Rocket Military Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Minna","middleName":"","lastName":"Gao","suffix":""},{"id":543088039,"identity":"9ea3526e-d156-4549-9816-cba7ebb6fa47","order_by":3,"name":"Rong Zhao","email":"","orcid":"","institution":"Chinese People’s Armed Police Force Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zhao","suffix":""},{"id":543088040,"identity":"781780e4-8a8e-4946-80c6-4f6ca55c2f3d","order_by":4,"name":"Fang Shen","email":"","orcid":"","institution":"Chinese People’s Armed Police Force Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Shen","suffix":""},{"id":543088041,"identity":"d58ad3eb-45ab-426d-8a1d-ef4c60beb7da","order_by":5,"name":"Mingqi Wang","email":"","orcid":"","institution":"Chinese People’s Armed Police Force Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Mingqi","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-02 14:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6803229/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6803229/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96242154,"identity":"ab136faf-6dfc-4538-a857-21ab82439107","added_by":"auto","created_at":"2025-11-19 07:12:13","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7167,"visible":true,"origin":"","legend":"","description":"","filename":"bad1d63f7162471890bddc811a541b3f.json","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/d9efebc0e426d621fb1a26c9.json"},{"id":95874209,"identity":"33a23a92-9b91-47fc-8ac6-114cd526c6b4","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84005,"visible":true,"origin":"","legend":"","description":"","filename":"bad1d63f7162471890bddc811a541b3f1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/674741aad3c12ae93cded30c.xml"},{"id":96241612,"identity":"3780ef0e-543c-4591-8aec-ccfae1533de9","added_by":"auto","created_at":"2025-11-19 07:11:08","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78626,"visible":true,"origin":"","legend":"","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/a435413ea42e6bd23d5bb7db.png"},{"id":96240813,"identity":"6d91a0de-ad2b-46b2-aa65-470fa5619a4b","added_by":"auto","created_at":"2025-11-19 07:09:36","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60525,"visible":true,"origin":"","legend":"","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/a74d1c59b56319e9df2225da.png"},{"id":96241722,"identity":"33a5e144-19b3-4dc4-82f0-d2cf8c5f631e","added_by":"auto","created_at":"2025-11-19 07:11:18","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":58901,"visible":true,"origin":"","legend":"","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/a99cabffd6e9e8ebde4b7605.png"},{"id":95874217,"identity":"701fd706-3da8-4d2f-9325-0b69a640ae05","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93471,"visible":true,"origin":"","legend":"","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/80cac7b44d069ce4279ba8ee.png"},{"id":96241923,"identity":"3183ae21-aa50-40fa-8746-3f6495c33d6b","added_by":"auto","created_at":"2025-11-19 07:11:43","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41793,"visible":true,"origin":"","legend":"","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/f10e2812e3ab3968b4f28b57.png"},{"id":96241905,"identity":"ca52a7bd-656d-40f9-9eab-a4fa6153eb9f","added_by":"auto","created_at":"2025-11-19 07:11:38","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35353,"visible":true,"origin":"","legend":"","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/50e8aa36151239c58336ab8f.png"},{"id":95874218,"identity":"8dfc482c-5953-4f88-8bc7-3f20617bddd3","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30930,"visible":true,"origin":"","legend":"","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/c2d762765c5ccaf87541a5f1.png"},{"id":95874219,"identity":"f05b50fe-6327-4606-a65e-a862a3dcf2f3","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134840,"visible":true,"origin":"","legend":"","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/257b4994a1ac389bf61592e8.png"},{"id":96242350,"identity":"9add7e18-50e9-4686-97a6-f4a41475faac","added_by":"auto","created_at":"2025-11-19 07:12:45","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":546710,"visible":true,"origin":"","legend":"","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/a6e21d2482c17636120ec6f3.png"},{"id":96241227,"identity":"741125c2-6b3f-4f39-8b32-6b72ba2de7df","added_by":"auto","created_at":"2025-11-19 07:10:24","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60608,"visible":true,"origin":"","legend":"","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/2dea1812c92c5f7b29303c10.png"},{"id":95874220,"identity":"30bfecc7-fc62-4c62-8d75-dda7d5e4468f","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57916,"visible":true,"origin":"","legend":"","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/7893b361ee65a3ba4b860e2d.png"},{"id":96241387,"identity":"9a74a814-1587-415d-958b-d1c04442da23","added_by":"auto","created_at":"2025-11-19 07:10:40","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28021,"visible":true,"origin":"","legend":"","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/10df288021e4827b969986ee.png"},{"id":95874227,"identity":"0635ea5a-d75e-4144-bc8e-84f3e6c34675","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1452919,"visible":true,"origin":"","legend":"","description":"","filename":"NetworktrafficpredictionmodelbasedonWTTBiLSTM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/1ae9ecdf51a2b054d1d7ed1d.pdf"},{"id":95874235,"identity":"b3a25370-def0-43a6-9920-8b4be55e502c","added_by":"auto","created_at":"2025-11-13 23:58:45","extension":"zip","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":743933,"visible":true,"origin":"","legend":"","description":"","filename":"code.zip","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/53c2837f7187666d46b0c6f5.zip"},{"id":95874239,"identity":"8b33add2-0bfc-4a1f-b0c6-2a21959741fa","added_by":"auto","created_at":"2025-11-13 23:58:45","extension":"zip","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10099036,"visible":true,"origin":"","legend":"","description":"","filename":"figures.zip","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/26ca9f88d067b27dc7eaacea.zip"},{"id":95874232,"identity":"fe633627-cd81-459d-84ec-367b89d11e4c","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"bst","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146013,"visible":true,"origin":"","legend":"","description":"","filename":"snapacite.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/bf2693287fa17bd912f71939.bst"},{"id":96241053,"identity":"65a33492-2846-40e1-aa1e-06e90a270bfd","added_by":"auto","created_at":"2025-11-19 07:09:57","extension":"bst","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29828,"visible":true,"origin":"","legend":"","description":"","filename":"snaps.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/6a7a86b60b6a6f1dd773bfad.bst"},{"id":95874225,"identity":"8cef2379-dc93-4a64-b07d-5b3dce3d3010","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"bbl","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10318,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.bbl","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/604e4408ba53fb70a4d37f1f.bbl"},{"id":95874229,"identity":"4d6983c5-38c3-491a-baeb-fafe0336a25f","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"pdf","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":421391,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/03b49065644cbaeec4133a26.pdf"},{"id":96241071,"identity":"9a2c593d-e471-450b-8cac-eed3d179fffb","added_by":"auto","created_at":"2025-11-19 07:10:00","extension":"bst","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35515,"visible":true,"origin":"","legend":"","description":"","filename":"snbasic.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/f91dbdd5165aa69772cf648a.bst"},{"id":96241867,"identity":"2cfdcb79-ce00-4498-a64e-3b65056778dd","added_by":"auto","created_at":"2025-11-19 07:11:32","extension":"bst","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33968,"visible":true,"origin":"","legend":"","description":"","filename":"snchicago.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/c623a1dd4f7cacc5a4fffc31.bst"},{"id":95874233,"identity":"7c8526c2-1092-4ec0-aafe-5c13317591d7","added_by":"auto","created_at":"2025-11-13 23:58:45","extension":"cls","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55857,"visible":true,"origin":"","legend":"","description":"","filename":"snjnl.cls","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/97cd85dcec6cd3226b49cfad.cls"},{"id":96242113,"identity":"2cf57f4b-4d65-4579-bb81-db274d6f3ac2","added_by":"auto","created_at":"2025-11-19 07:12:06","extension":"bst","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64023,"visible":true,"origin":"","legend":"","description":"","filename":"snmathphysay.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/440e057cbf2a62ffd9ac791f.bst"},{"id":96241548,"identity":"26eecf6f-2d0d-44ea-8578-9b1ee9e2d4ae","added_by":"auto","created_at":"2025-11-19 07:10:57","extension":"bst","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64166,"visible":true,"origin":"","legend":"","description":"","filename":"snmathphysnum.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/076fd33850af6bd9e7681112.bst"},{"id":96241146,"identity":"bee91735-c550-4307-a110-18c1fe971926","added_by":"auto","created_at":"2025-11-19 07:10:20","extension":"bst","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37333,"visible":true,"origin":"","legend":"","description":"","filename":"snnature.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/b4983a0b9154dbd056ef5975.bst"},{"id":96242092,"identity":"03f7015a-f6bd-4fec-a9d9-d2d4e28a369b","added_by":"auto","created_at":"2025-11-19 07:11:59","extension":"bst","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39951,"visible":true,"origin":"","legend":"","description":"","filename":"snvancouveray.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/e49d1a5b54f66b5938379a98.bst"},{"id":95874236,"identity":"2193ebde-7cb7-443e-94aa-69f35e6ecb4c","added_by":"auto","created_at":"2025-11-13 23:58:45","extension":"bst","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40758,"visible":true,"origin":"","legend":"","description":"","filename":"snvancouvernum.bst","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/3d04995fd7653a3b8074db28.bst"},{"id":95874237,"identity":"c04d6b8f-0546-477d-8e86-36408fffc2ec","added_by":"auto","created_at":"2025-11-13 23:58:45","extension":"pdf","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":418495,"visible":true,"origin":"","legend":"","description":"","filename":"usermanual.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/30df8849e902bb5d61655b48.pdf"},{"id":95874241,"identity":"f295ef9b-727d-4257-ac00-87b4d063817b","added_by":"auto","created_at":"2025-11-13 23:58:45","extension":"xml","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100411,"visible":true,"origin":"","legend":"","description":"","filename":"bad1d63f7162471890bddc811a541b3f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/a367bd72fcb6c89c3ac9944f.xml"},{"id":96241563,"identity":"bc492123-04b9-4829-8c0c-a5c7a9e29530","added_by":"auto","created_at":"2025-11-19 07:10:57","extension":"html","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113217,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/dcab380bdbeedbd898e08802.html"},{"id":103503922,"identity":"5f0fc9ac-17cb-4dfe-bef2-f9829f54d6a6","added_by":"auto","created_at":"2026-02-26 13:04:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":769532,"visible":true,"origin":"","legend":"","description":"","filename":"NetworktrafficpredictionmodelbasedonWTTBiLSTM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1_covered_42b68b9a-6706-45c2-b561-dfa0c9f13d62.pdf"},{"id":95874216,"identity":"0a4d1983-78f0-42d0-bacc-d152b62bc4a2","added_by":"auto","created_at":"2025-11-13 23:58:44","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":743933,"visible":true,"origin":"","legend":"","description":"","filename":"code.zip","url":"https://assets-eu.researchsquare.com/files/rs-6803229/v1/3d75f208804f0a57e49bb12e.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network traffic prediction model based on WT-TBiLSTM","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"WT-TBiLSTM, Adaptive soft-thresholding, Topology attention, Network traffic prediction","lastPublishedDoi":"10.21203/rs.3.rs-6803229/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6803229/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate short-term network traffic prediction is critical for proactive network security management, yet remains challenging due to the inherent complexity, noise, and non-stationarity of traffic data. To address this, we propose WT-TBiLSTM, a hybrid deep learning model integrating Wavelet Transform (WT) for noise reduction and a Topology-Attention-enhanced Bidirectional LSTM (TBiLSTM) for temporal feature extraction. First, the WT module decomposes raw traffic data using Daubechies8 wavelets, applies adaptive soft-thresholding to suppress noise, and reconstructs denoised signals through multi-scale analysis. The TBiLSTM module then captures bidirectional temporal dependencies while leveraging a topology attention mechanism to emphasize local structural patterns, enhancing sensitivity to abrupt traffic variations. Experiments on two real-world datasets (UK\\_Academic and EU\\_Core) demonstrate the model\u0026rsquo;s superiority. Compared to baseline models , WT-TBiLSTM reduces prediction errors by 64.37% RMSE, 65.45% MAE, and 67.89% MAPE on UK\\_Academic, and by 37.26% RMSE, 39.35% MAE, and 38.76% MAPE on EU\\_Co. The integration of wavelet denoising and topology attention significantly improves robustness against noise and captures multi-scale temporal dynamics, enabling precise short-term predictions. This work provides a practical solution for real-time network traffic forecasting, empowering administrators to preemptively mitigate security risks and optimize resource allocation.\u003c/p\u003e","manuscriptTitle":"Network traffic prediction model based on WT-TBiLSTM","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 23:58:39","doi":"10.21203/rs.3.rs-6803229/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":"534983d6-e728-45f5-a3f8-99c5dc6e7ed4","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57775035,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":57775036,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2026-02-10T11:47:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 23:58:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6803229","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6803229","identity":"rs-6803229","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}