An Adaptive Data-Driven Framework for Real-Time Cyber Threat Detection and Response

An Adaptive Data-Driven Framework for Real-Time Cyber Threat Detection and Response | 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 An Adaptive Data-Driven Framework for Real-Time Cyber Threat Detection and Response Rajiv Iyer, Priyant Banerjee, Deepika Shekhawat, A. Saranya, Garima Shukla, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8838452/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 The conventional model of cybersecurity, founded on fixed sets of rules and labelled sets, demonstrates quite limited performance in the face of the uncertainty of zero-day attacks and operational imperatives introduced by high false-positive rates and slow reactions. Contrarily, the RTACD model is a major improvement. RTACD, which is based on the supervised, semi-supervised, and unsupervised machine learning, allows detecting the threats immediately and responding to them dynamically and contextually. Notably, compared to the earlier models, which heavily depended on the presence of pre-existing threat signatures or large volumes of pre-collected labelled data, RTACD constantly learns on the latest data streams and uses reinforcement learning to tighten the screws. The preliminary tests, such as testing on the CICIDS2017 dataset, and real-world deployments to healthcare, finance, and government, establish the following improvements: false positives have been reduced by 30%, ransomware cases by 75%, and the accuracy of existing threats is above 98% and that of the zero-day anomalies above 92%. The gist is that RTACD framework presents a scalable and dynamic solution that improves the resilience of organizations and the continuity of their operations and closes long-term vulnerabilities of conventional approaches to cybersecurity. Physical sciences/Engineering Physical sciences/Mathematics and computing 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. 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-8838452","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596120536,"identity":"9dbacedb-1f65-418b-a338-74794e0f4681","order_by":0,"name":"Rajiv Iyer","email":"","orcid":"","institution":"Amity University Mumbai","correspondingAuthor":false,"prefix":"","firstName":"Rajiv","middleName":"","lastName":"Iyer","suffix":""},{"id":596120537,"identity":"874e9a47-8ab2-47da-9953-2a9d375dd4c5","order_by":1,"name":"Priyant Banerjee","email":"","orcid":"","institution":"Amity University Mumbai","correspondingAuthor":false,"prefix":"","firstName":"Priyant","middleName":"","lastName":"Banerjee","suffix":""},{"id":596120538,"identity":"31d0e0dd-eafa-409b-9caa-e0522a03e35c","order_by":2,"name":"Deepika Shekhawat","email":"","orcid":"","institution":"Amity University Mumbai","correspondingAuthor":false,"prefix":"","firstName":"Deepika","middleName":"","lastName":"Shekhawat","suffix":""},{"id":596120539,"identity":"35e3a146-9ff3-4ad3-a03b-22d4519e130e","order_by":3,"name":"A. 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