{"paper_id":"2c852d6e-0c4d-4a68-a00b-cc42e8536b27","body_text":"AI-Driven Cybersecurity Situational Awareness for IoT Networks: Enhancing Threat Detection and Prediction with Machine Learning | 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 AI-Driven Cybersecurity Situational Awareness for IoT Networks: Enhancing Threat Detection and Prediction with Machine Learning Hafiz Muhammad Jamsheed Nazir, Omar I. Alsaleh, Noor Hidayah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7731888/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 With the rapid development of network technologies, emerging innovations such as the Internet of Things (IoT), cloud computing, and big data are becoming increasingly widespread. However, this proliferation has also intensified cybersecurity threats in cyberspace. As a result, cyber security situational awareness (CSA) has attracted significant attention from both academia and industry. Typically, CSA models comprise three key levels: element extraction, situational understanding, and situational prediction. Among these, situational prediction which involves forecasting the overall security posture of networks holds critical theoretical and practical value. This paper investigates how artificial neural networks and inverse transactions can be effectively leveraged to enhance cybersecurity situational awareness. By integrating these advanced technologies, the study aims to improve predictive and responsive capabilities against cyber threats, thereby strengthening network defense systems and addressing the ever-evolving landscape of cyber risks. To address the gap in theoretical modeling for CSA, a novel cybersecurity situational awareness framework is proposed. Its effectiveness is evaluated across several dimensions, including model structure, knowledge representation, and assessment methods. Improved two-party and three-party authentication schemes tailored for IoT environments are introduced, alongside a machine learning-based authentication framework. These approaches, combined with enhanced identification techniques, outperform traditional methods in terms of accuracy. Furthermore, the study explores the application of these methods in key areas such as security, data transmission, system survivability, and comprehensive system assessment showcasing the latest advancements and outlining future directions in CSA research. Network security situation awareness Internet of Things Artificial neural network Backpropagation 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. 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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-7731888\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":582061193,\"identity\":\"f40553ae-581c-4181-b800-92521545b046\",\"order_by\":0,\"name\":\"Hafiz Muhammad Jamsheed Nazir\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACxgYwxcbAfADGJlYLG1sCsVpggI3HgDgt8u2HGx8X1Ngw8Mn3fJP4ucNGjoH98NEN+LQYnElsNp5xLA3oMN5tkr1n0owZeNLSbuDVIsHYJs3bcBisRYK37XBigwSPGV4t8jMY23/zNvwHauF5JvmXGC0MNxjbmHkbDoC0sEkTZQvIL9I8x5J52NjSjK1l29KM2Qj5Rb79+MPPPDV2cvLNhx/efNtmI8fPfvgYfodBAQ8Qs0iAWGzEKIcB5g+kqB4Fo2AUjIKRAwD+f0AyaLB4DgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Northwestern Polytechnical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hafiz\",\"middleName\":\"Muhammad Jamsheed\",\"lastName\":\"Nazir\",\"suffix\":\"\"},{\"id\":582061194,\"identity\":\"d0388d82-a703-4b62-bf80-4341860aa913\",\"order_by\":1,\"name\":\"Omar I. 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However, this proliferation has also intensified cybersecurity threats in cyberspace. As a result, cyber security situational awareness (CSA) has attracted significant attention from both academia and industry. Typically, CSA models comprise three key levels: element extraction, situational understanding, and situational prediction. Among these, situational prediction which involves forecasting the overall security posture of networks holds critical theoretical and practical value. This paper investigates how artificial neural networks and inverse transactions can be effectively leveraged to enhance cybersecurity situational awareness. By integrating these advanced technologies, the study aims to improve predictive and responsive capabilities against cyber threats, thereby strengthening network defense systems and addressing the ever-evolving landscape of cyber risks.\\u003c/p\\u003e \\u003cp\\u003eTo address the gap in theoretical modeling for CSA, a novel cybersecurity situational awareness framework is proposed. Its effectiveness is evaluated across several dimensions, including model structure, knowledge representation, and assessment methods. Improved two-party and three-party authentication schemes tailored for IoT environments are introduced, alongside a machine learning-based authentication framework. These approaches, combined with enhanced identification techniques, outperform traditional methods in terms of accuracy. 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