Intelligent Intrusion Detection Using Signature Analysis and Predictive Cognitive Threat Engine

Intelligent Intrusion Detection Using Signature Analysis and Predictive Cognitive Threat Engine | 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 Intelligent Intrusion Detection Using Signature Analysis and Predictive Cognitive Threat Engine Hemanth Uppala, Renuga Devi R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8791374/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract The fast development of the network traffic and the dynamism of contemporary communication systems have generated an urgent requirement of intelligent structures that have the ability to detect, analyze and foresee cyber threats rightly. Conventional intrusion detection and classification strategies tend to fail with massive network data under adversarial conditions as well as that changing with time. To improve security, accuracy, and predictive power, the proposed paper will present a new multi-stage cyber threat framework based on deep learning, quantum-inspired modeling, evolutionary optimization, and graph-based reasoning. Adversarial-Aware Cognitive Data Refinement (AA-CDR) is initially used to preprocess network data to remove noise, save adversarial exemplification, and reconstruct minority attack examples to produce attack-aware datasets. The Quantum-Enhanced Temporal Behaviour Modeling (QETBM) represents both short and long-term node behaviour of sequential traffic. Causally significant and discriminative features are then determined by Causal Evolutionary Feature Synthesis (CE-FS). Graph-Centric Attack Reasoning Classifier (GCARC) relates structural graph relationships to identify normal and malicious activities, whereas the Predictive Cognitive Threat Intelligence Engine (PCTIE) predicts possible threats. The evaluations on the experiments indicate considerable advancement in detection accuracy, precision, recall, feature reduction, and computational efficiency over the traditional ML-AIDS, ANN and PCA methods and thus the framework has been demonstrated to be highly adaptable to dynamic and large-scale network security settings. Cyber Threat Detection Adversarial-Aware Data Refinement Quantum-Enhanced Behaviour Modeling Causal -evolutionary Feature Selection Graph-Centric Attack Classification Predictive Threat Intelligence Deep Learning Evolutionary Optimization Network Security Attack Prediction Temporal Behaviour Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor assigned by journal 21 Feb, 2026 Submission checks completed at journal 21 Feb, 2026 First submitted to journal 04 Feb, 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. 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-8791374","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600017756,"identity":"38fb2926-ff23-4718-b22a-fe890d30bc27","order_by":0,"name":"Hemanth Uppala","email":"","orcid":"","institution":"SRM Institute of Science and Technology Ramapuram Campus","correspondingAuthor":false,"prefix":"","firstName":"Hemanth","middleName":"","lastName":"Uppala","suffix":""},{"id":600017757,"identity":"c7fe4fe5-096f-40f7-95c7-6e523f7fa7de","order_by":1,"name":"Renuga Devi R","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3OuwrCMBSA4SNCXApdUxR9hSOFinTwVRoEuxT0ARyc6uLlVXQRx0KhU9U1bjro5CKIVChiWhxcbHUTzD/kAucjAZDJfrciqMlmwXO1PiHaIB3GLwh66QEhd74xHAfapW929c3qMNvFMaglB2G/fE8q4bpTrgR2c8FtgzMXQRudEFj4nlDqGGVKfDQ4IZwNEJCLV5ibQWonQe4+6tOAcCtGaOUSqhja2fURoSMIEa/QPKI4ulmY2Ei5IMzVFRoee14mKYX1bXQ1URUf20ZxtaoO2/P9LYMkFZWXS3r2sgFAIcqbkMlksv/uAQy4TIvrChiyAAAAAElFTkSuQmCC","orcid":"","institution":"SRM Institute of Science and Technology Ramapuram Campus","correspondingAuthor":true,"prefix":"","firstName":"Renuga","middleName":"Devi","lastName":"R","suffix":""}],"badges":[],"createdAt":"2026-02-05 02:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8791374/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8791374/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104403263,"identity":"44b2aebf-60ff-4f0c-a1d2-289fe5f521e9","added_by":"auto","created_at":"2026-03-11 12:17:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":600578,"visible":true,"origin":"","legend":"","description":"","filename":"PAPER.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8791374/v1_covered_8e33ac99-1f80-4675-9352-8f21c3a9745d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIntelligent Intrusion Detection Using Signature Analysis and Predictive Cognitive Threat Engine\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":"journal-of-computer-virology-and-hacking-techniques","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jicv","sideBox":"Learn more about [Journal of Computer Virology and Hacking Techniques](http://link.springer.com/journal/11416)","snPcode":"11416","submissionUrl":"https://submission.springernature.com/new-submission/11416/3","title":"Journal of Computer Virology and Hacking Techniques","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cyber Threat Detection, Adversarial-Aware Data Refinement, Quantum-Enhanced Behaviour Modeling, Causal -evolutionary Feature Selection, Graph-Centric Attack Classification, Predictive Threat Intelligence, Deep Learning, Evolutionary Optimization, Network Security, Attack Prediction, Temporal Behaviour Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8791374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8791374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe fast development of the network traffic and the dynamism of contemporary communication systems have generated an urgent requirement of intelligent structures that have the ability to detect, analyze and foresee cyber threats rightly. 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