Integrating Machine Learning and Artificial Intelligence for Next-Generation Cybersecurity in Computer Science Applications

Integrating Machine Learning and Artificial Intelligence for Next-Generation Cybersecurity in Computer Science Applications | 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 Integrating Machine Learning and Artificial Intelligence for Next-Generation Cybersecurity in Computer Science Applications Naveed Akhtar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7703315/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 Cybersecurity remains one of the most pressing challenges in computer science as cyberattacks grow increasingly sophisticated, leveraging automation and adversarial intelligence. Traditional security mechanisms, including signature-based intrusion detection and rule-driven access control, often fail against zero-day exploits and advanced persistent threats due to their static nature and limited adaptability. Existing research in machine learning (ML) and artificial intelligence (AI) for cybersecurity has produced notable advancements, yet most frameworks remain constrained by isolated modeling, poor scalability, or high false-positive rates. To address these limitations, this study proposes a hybrid deep learning framework, CS-MLAI-Net , that integrates convolutional neural networks (CNNs) with bidirectional long short-term memory (BiLSTM) architectures, enabling both feature extraction and temporal attack pattern recognition. We evaluate CS-MLAI-Net on the NSL-KDD and CICIDS-2017 datasets , which encompass diverse modern intrusion types including denial-of-service, brute force, and infiltration attacks. Data preprocessing included normalization, categorical encoding, and synthetic minority oversampling to balance class distributions. Experimental results demonstrate superior detection accuracy of 98.7% , precision of 97.9% , and an F1-score of 98.2% , outperforming existing state-of-the-art methods by a significant margin while reducing false positives. The main contributions include a novel hybrid architecture tailored for cybersecurity, comprehensive benchmarking across multiple datasets, and robust preprocessing strategies to enhance generalizability. This work highlights the potential of AI-driven cybersecurity in securing next-generation digital infrastructures. Future research will extend CS-MLAI-Net towards real-time deployment in large-scale, cloud-native environments. Theoretical Computer Science Cybersecurity Machine Learning Artificial Intelligence Hybrid Deep Learning Network Security Threat Intelligence 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-7703315","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519952839,"identity":"1db7b8c9-371d-4e25-82f9-c91950e2f7e2","order_by":0,"name":"Naveed 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Traditional security mechanisms, including signature-based intrusion detection and rule-driven access control, often fail against zero-day exploits and advanced persistent threats due to their static nature and limited adaptability. Existing research in machine learning (ML) and artificial intelligence (AI) for cybersecurity has produced notable advancements, yet most frameworks remain constrained by isolated modeling, poor scalability, or high false-positive rates. To address these limitations, this study proposes a hybrid deep learning framework, \u003cb\u003eCS-MLAI-Net\u003c/b\u003e, that integrates convolutional neural networks (CNNs) with bidirectional long short-term memory (BiLSTM) architectures, enabling both feature extraction and temporal attack pattern recognition. We evaluate CS-MLAI-Net on the \u003cb\u003eNSL-KDD and CICIDS-2017 datasets\u003c/b\u003e, which encompass diverse modern intrusion types including denial-of-service, brute force, and infiltration attacks. 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