Q-SSC³-IP: Quantum-Inspired Semi-Supervised Sparse Subspace Clustering with Contextual Coherence for Intrusion Prevention

Q-SSC³-IP: Quantum-Inspired Semi-Supervised Sparse Subspace Clustering with Contextual Coherence for Intrusion Prevention | 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 Q-SSC³-IP: Quantum-Inspired Semi-Supervised Sparse Subspace Clustering with Contextual Coherence for Intrusion Prevention Lathika V, Sakthivel S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8733603/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract The scale of large-scale, heterogeneous and high-dimensional network traffic has posed a serious challenge to the efficacy of conventional intrusion detection and prevention infrastructures, especially when the volume of labelled information is small, as well as when the attack patterns are constantly changing. Current supervised and deep learning-based security models have high labelling rates, lower generalization as well as higher false alarms in the face of stealthy or overlapping intrusion patterns. To overcome these shortcomings, this paper suggests Q-SSC³-IP as a quantum-inspired semi-supervised sparse subspace clustering scheme, with contextual coherence to intelligent network intrusion prevention. The suggested solution converts network traffic into a Hilbert feature space inspired by quantum computing, which allows the similarity modelling of interference to be performed to better distinguish between innocent and malicious connections. Semi-supervised sparse subspace learning mechanism is utilized to utilize labelled and unlabelled traffic samples in order to discover latent behavioural subspaces when under high-dimensional conditions. The regularization of contextual coherence also deals with temporal and statistical dependencies in between sequential connections to enable proactive prevention of intrusion other than passive detection. To prevent local minima, the quantum-inspired annealing is used in the optimization process in order to achieve stable convergence. The experimental analysis on NSL-KDD benchmark dataset show that the proposed framework yields a detection accuracy of 96.87, an F1-score of 96.11 along with a lower false alarm rate of 2.84, which is better than the state-of-the-art supervised, unsupervised and hybrid approaches to intrusion detection. The findings prove that Q-SSC³-IP is a scalable, explainable as well as robust solution to next-generation intrusion prevention systems that can be used under realistic network conditions Physical sciences/Engineering Physical sciences/Mathematics and computing Intrusion Prevention Quantum-Inspired Learning Semi-Supervised Clustering Sparse Subspace Modelling Contextual Coherence NSL-KDD Network Security Anomaly Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 11 Feb, 2026 Editor invited by journal 09 Feb, 2026 Editor assigned by journal 04 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 29 Jan, 2026 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-8733603","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590134924,"identity":"6bc0937a-37a4-4765-8a41-54207e1d7bad","order_by":0,"name":"Lathika V","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACPgYGNoaEA0AWO5hvA8SMjQfwaWFjYIZqYQbz00BaGghrYUBoOQwm8WthP3/swYMzdon9zczPJH7uOG+3tv0w0JYam2icWniS2Q0SbiQnzjjMZibZe+Z28rYziUAtx9JyG3A6LJlNIuEDszHQScYGvG23k80OALUwNhzGrYX/MUhLvbH8YfbPhn/bziWbnX9IQIsEyJYbh+UMDvMYPuZtO2BndoOQLRKPzSQSzhyXMzzMU/hYti05wewG0JYEPH7h5098JvnjWDWP3PH2DQffttnZm51Pf/jgQ40NTi0YIBGsMoFY5SBgT4riUTAKRsEoGBkAAGo/YMf3gmSRAAAAAElFTkSuQmCC","orcid":"","institution":"Sona College of Technology","correspondingAuthor":true,"prefix":"","firstName":"Lathika","middleName":"","lastName":"V","suffix":""},{"id":590134925,"identity":"8c90e108-6251-41b0-a0ed-b8407d833d2f","order_by":1,"name":"Sakthivel S","email":"","orcid":"","institution":"Sona College of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sakthivel","middleName":"","lastName":"S","suffix":""}],"badges":[],"createdAt":"2026-01-29 16:11:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8733603/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8733603/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963478,"identity":"249738a9-daaa-4064-9905-2ec9ce3492af","added_by":"auto","created_at":"2026-02-19 04:18:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1264534,"visible":true,"origin":"","legend":"","description":"","filename":"1QSSCIPQuantumInspiredSemiSupervised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8733603/v1_covered_53125d60-51d3-425f-b746-b31e8014988c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eQ-SSC³-IP: Quantum-Inspired Semi-Supervised Sparse Subspace Clustering with Contextual Coherence for Intrusion Prevention\u003c/strong\u003e\u003c/p\u003e","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intrusion Prevention, Quantum-Inspired Learning, Semi-Supervised Clustering, Sparse Subspace Modelling, Contextual Coherence, NSL-KDD, Network Security, Anomaly Detection","lastPublishedDoi":"10.21203/rs.3.rs-8733603/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8733603/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe scale of large-scale, heterogeneous and high-dimensional network traffic has posed a serious challenge to the efficacy of conventional intrusion detection and prevention infrastructures, especially when the volume of labelled information is small, as well as when the attack patterns are constantly changing. Current supervised and deep learning-based security models have high labelling rates, lower generalization as well as higher false alarms in the face of stealthy or overlapping intrusion patterns. To overcome these shortcomings, this paper suggests Q-SSC\u0026sup3;-IP as a quantum-inspired semi-supervised sparse subspace clustering scheme, with contextual coherence to intelligent network intrusion prevention. The suggested solution converts network traffic into a Hilbert feature space inspired by quantum computing, which allows the similarity modelling of interference to be performed to better distinguish between innocent and malicious connections. Semi-supervised sparse subspace learning mechanism is utilized to utilize labelled and unlabelled traffic samples in order to discover latent behavioural subspaces when under high-dimensional conditions. The regularization of contextual coherence also deals with temporal and statistical dependencies in between sequential connections to enable proactive prevention of intrusion other than passive detection. To prevent local minima, the quantum-inspired annealing is used in the optimization process in order to achieve stable convergence. The experimental analysis on NSL-KDD benchmark dataset show that the proposed framework yields a detection accuracy of 96.87, an F1-score of 96.11 along with a lower false alarm rate of 2.84, which is better than the state-of-the-art supervised, unsupervised and hybrid approaches to intrusion detection. 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