Next-Generation Intrusion Detection Systems: A Hybrid Machine Learning Framework for Intelligent Cyber Threat Neutralization

Next-Generation Intrusion Detection Systems: A Hybrid Machine Learning Framework for Intelligent Cyber Threat Neutralization | 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 Next-Generation Intrusion Detection Systems: A Hybrid Machine Learning Framework for Intelligent Cyber Threat Neutralization Ankit Kumar, Abhishek kumar, Rohit Raja, Amit Kumar Dewangan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5762323/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 In the rapidly evolving landscape of cybersecurity, network intrusion detection systems (NIDS) face significant challenges in effectively identifying and mitigating sophisticated cyber threats. In this research, we propose an innovative hybrid approach that combines signature-based detection, anomaly-based detection and LSTM model as a substantial solution to the limitations faced in existing intrusion detection methodologies. The hybrid intrusion detection system is a game changer in threat detection capabilities. The research addresses the inherent weaknesses of traditional single-method approaches by combining multiple detection methodologies. Signature-based detection works well for known threats but is ineffective against zero-day attacks, and anomaly-based detection produces high false positive rates. This innovative hybrid model utilizes machine learning as a smart filtering mechanism to fill these crucial voids. Extensive simulations and in depth statistical analysis show impressive performance gains. The system attained a true positive detection rate of 98% which is a significant improvement over previous methods whilst reducing the final false positive rates by approximately 70%. The performance metrics define the efficiency of the system with 98% detection accuracy, significant reduction in false positive rates and increased threat recognition at known and unknown attack surface. The proposed system integrates a variety of detection methods along with refined machine learning approaches to provide an overall intelligent and adaptive network security framework, making it a strong candidate for advanced intrusion detection systems. Intrusion Detection Systems Cyber Security Hybrid Models Machine Learning 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-5762323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398259007,"identity":"582a7b54-5f30-408f-90ff-da727ad70363","order_by":0,"name":"Ankit 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In this research, we propose an innovative hybrid approach that combines signature-based detection, anomaly-based detection and\u0026ensp;LSTM model as a substantial solution to the limitations faced in existing intrusion detection methodologies. The hybrid\u0026ensp;intrusion detection system is a game changer in threat detection capabilities. The research addresses the inherent weaknesses of traditional single-method approaches by combining\u0026ensp;multiple detection methodologies. Signature-based detection works well for known threats but is ineffective against zero-day attacks, and\u0026ensp;anomaly-based detection produces high false positive rates. This innovative\u0026ensp;hybrid model utilizes machine learning as a smart filtering mechanism to fill these crucial voids. Extensive simulations and\u0026ensp;in depth statistical analysis show impressive performance gains. The system attained a true positive\u0026ensp;detection rate of 98% which is a significant improvement over previous methods whilst reducing the final false positive rates by approximately 70%. The performance metrics define the\u0026ensp;efficiency of the system with 98% detection accuracy, significant reduction in false positive rates and increased threat recognition at known and unknown attack surface. 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