IDS Based on Deep Learning Algorithms: Design, Implementation, and Performance Evaluation

IDS Based on Deep Learning Algorithms: Design, Implementation, and Performance Evaluation | 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 IDS Based on Deep Learning Algorithms: Design, Implementation, and Performance Evaluation Priya Bhashini Koyyagura, V V S S S Balaram, Lokendra Singh Umrao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6375510/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 IDSs will play a big role in defending network infrastructures from the ever-increasing development and sophistication of cyber threats. As attack vectors continue to increase in complexity, more traditional intrusion-detection-based techniques encounter difficulties with effective identification of attacks at the same time as reducing false positives. This paper explores conceptualization, implementation, and assessment of an Intrusion Detection System (IDS) using a change of advanced deep learning algorithms, which includes Simple RNN, LTM, GRU, CNN, and Hybrid architecture. The respective models are trained and fine-tuned on the NF-UNSW-NB15 dataset so they can have high detection precision as well as strong performance. All the deep learning algorithms were estimated in terms of detection accuracy, precision, recall, F1 score, and training time. The CNN-based IDS recorded the highest detection accuracy at 98.59% coupled with an F1 score of 84.2%, which proves its potential for real-time detection scenarios. The Hybrid model displayed good accuracy (86.31%) and recall (78.44%) values, however, its training took a lot of time due to the complexity needed for combining multiple architectures. Both the LTM and GRU achieved similar performance. For the former, accuracy attained 98.28%, and F1 was above 79%. It seems that the Simple RNN model has lower recall and F1, suggesting that certain kinds of attacks may have been missed. This paper summaries in what way deep learning models can help improve the precision and performance of an IDS. Intrusion Detection System Deep Learning CNN Gated Recurrent Units NF-UNSW-NB15 Dataset 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. 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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-6375510","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438775027,"identity":"3d7e1323-f577-4992-a16b-f6fa2e0fd588","order_by":0,"name":"Priya Bhashini Koyyagura","email":"","orcid":"","institution":"Anurag University","correspondingAuthor":false,"prefix":"","firstName":"Priya","middleName":"Bhashini","lastName":"Koyyagura","suffix":""},{"id":438775028,"identity":"b76cc051-d9c9-436b-bcc5-5bee6f3e7f29","order_by":1,"name":"V V S S S Balaram","email":"","orcid":"","institution":"Anurag University","correspondingAuthor":false,"prefix":"","firstName":"V","middleName":"V S S S","lastName":"Balaram","suffix":""},{"id":438775029,"identity":"e5564013-19eb-4bbf-93f6-838b8a9ac5a8","order_by":2,"name":"Lokendra Singh Umrao","email":"data:image/png;base64,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","orcid":"","institution":"Rammanohar Lohia Avadh University","correspondingAuthor":true,"prefix":"","firstName":"Lokendra","middleName":"Singh","lastName":"Umrao","suffix":""}],"badges":[],"createdAt":"2025-04-04 10:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6375510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6375510/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80471301,"identity":"02ea75bf-7203-4b6a-99e0-bf427c3f53f8","added_by":"auto","created_at":"2025-04-13 08:01:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":474060,"visible":true,"origin":"","legend":"","description":"","filename":"PriyaIMPLPaperfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6375510/v1_covered_a562f4ce-d94d-45ea-902d-caf3a5e8b9e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIDS Based on Deep Learning Algorithms: Design, Implementation, and Performance Evaluation\u003c/p\u003e","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 System, Deep Learning, CNN, Gated Recurrent Units, NF-UNSW-NB15 Dataset","lastPublishedDoi":"10.21203/rs.3.rs-6375510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6375510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIDSs will play a big role in defending network infrastructures from the ever-increasing development and sophistication of cyber threats. As attack vectors continue to increase in complexity, more traditional intrusion-detection-based techniques encounter difficulties with effective identification of attacks at the same time as reducing false positives. This paper explores conceptualization, implementation, and assessment of an Intrusion Detection System (IDS) using a change of advanced deep learning algorithms, which includes Simple RNN, LTM, GRU, CNN, and Hybrid architecture. The respective models are trained and fine-tuned on the NF-UNSW-NB15 dataset so they can have high detection precision as well as strong performance. All the deep learning algorithms were estimated in terms of detection accuracy, precision, recall, F1 score, and training time. The CNN-based IDS recorded the highest detection accuracy at 98.59% coupled with an F1 score of 84.2%, which proves its potential for real-time detection scenarios. The Hybrid model displayed good accuracy (86.31%) and recall (78.44%) values, however, its training took a lot of time due to the complexity needed for combining multiple architectures. Both the LTM and GRU achieved similar performance. For the former, accuracy attained 98.28%, and F1 was above 79%. It seems that the Simple RNN model has lower recall and F1, suggesting that certain kinds of attacks may have been missed. 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