Deep Learning Approaches for Intelligent Intrusion Detection Systems: Bridging Computer Science and Cybersecurity

Deep Learning Approaches for Intelligent Intrusion Detection Systems: Bridging Computer Science and Cybersecurity | 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 Deep Learning Approaches for Intelligent Intrusion Detection Systems: Bridging Computer Science and Cybersecurity Nasir Abbas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7711258/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 Intrusion Detection Systems (IDS) are a cornerstone of modern cybersecurity, designed to safeguard networks from increasingly sophisticated attacks. Traditional machine learning–based IDS approaches often suffer from limited feature representation, high false alarm rates, and difficulty in adapting to evolving threat landscapes. To address these limitations, this study introduces DeepIDS-Net, a deep learning–driven intrusion detection framework that integrates convolutional and recurrent neural architectures to capture both spatial and temporal dependencies in network traffic. The proposed model was trained and evaluated on the widely adopted NSL-KDD dataset, which contains labeled records of normal and malicious activities across multiple attack categories. Preprocessing steps included data normalization, categorical encoding of protocol and service attributes, and feature scaling to ensure stable training. Experimental evaluation demonstrated that DeepIDS-Net achieved an accuracy of 98.4%, a precision of 97.9%, a recall of 98.1%, and an F1-score of 98.0%, significantly outperforming baseline models such as Random Forest, SVM, and standard deep feedforward networks. Key contributions include: (1) a hybrid deep architecture optimized for intrusion detection, (2) a systematic preprocessing pipeline enhancing learning efficiency, and (3) empirical evidence of reduced false positives while maintaining high detection sensitivity. The results highlight the potential of deep learning to transform IDS into adaptive, intelligent defense mechanisms. This work not only bridges computer science and cybersecurity but also provides a scalable pathway for real-world deployment of AI-enhanced IDS. Future research will extend this approach to large-scale, real-time streaming data for next-generation cyber defense. Theoretical Computer Science Intrusion Detection System Deep Learning Cybersecurity NSL-KDD Convolutional Neural Networks Full Text Additional Declarations The authors declare no competing interests. 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-7711258","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":520458691,"identity":"775703be-2fc8-4dbe-b2fa-c4b072089c97","order_by":0,"name":"Nasir 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Traditional machine learning\u0026ndash;based IDS approaches often suffer from limited feature representation, high false alarm rates, and difficulty in adapting to evolving threat landscapes. To address these limitations, this study introduces DeepIDS-Net, a deep learning\u0026ndash;driven intrusion detection framework that integrates convolutional and recurrent neural architectures to capture both spatial and temporal dependencies in network traffic. The proposed model was trained and evaluated on the widely adopted NSL-KDD dataset, which contains labeled records of normal and malicious activities across multiple attack categories. Preprocessing steps included data normalization, categorical encoding of protocol and service attributes, and feature scaling to ensure stable training. 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