Deep Belief Networks for Feature Learning in VANET Security Analysis

Deep Belief Networks for Feature Learning in VANET Security Analysis | 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Article Deep Belief Networks for Feature Learning in VANET Security Analysis Betty Heleen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7109296/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 Vehicular ad hoc networks (VANETs) play a critical role in enabling intelligent transportation systems, but their open and dynamic nature also makes them highly vulnerable to security threats. Traditional rule-based security mechanisms often fall short of identifying complex or evolving attack patterns. This study explores the application of Deep Belief Networks (DBNs) for automated feature learning and anomaly detection in VANET environments. By leveraging the hierarchical structure of DBNs, the model is trained to extract meaningful temporal and spatial features from high-dimensional traffic and communication data. These learned representations are then used to identify potential malicious behaviors such as spoofing, Sybil attacks, and false message injections. Experimental evaluations on benchmark VANET datasets demonstrate that DBNs significantly improve detection accuracy while reducing false positives compared to shallow learning approaches. This work highlights the potential of deep learning-based models in building more adaptive and resilient VANET security frameworks. Artificial Intelligence and Machine Learning Electrical Engineering Deep Belief Networks VANET Security Feature Learning Anomaly Detection Intelligent Transportation Systems Temporal-Spatial Analysis Sybil Attack Deep Learning in VANETs 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. 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