Collaborative Detection Framework Using ML for VANET Malicious Node Localization

Collaborative Detection Framework Using ML for VANET Malicious Node Localization | 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 Collaborative Detection Framework Using ML for VANET Malicious Node Localization Betty Heleen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7109228/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 vital role in enabling intelligent transportation systems by facilitating real-time communication among vehicles. However, the open and dynamic nature of VANETs makes them highly vulnerable to malicious nodes, which can disrupt communication, compromise safety, and degrade network performance. This paper proposes a collaborative detection framework that leverages machine learning (ML) techniques for the accurate localization of malicious nodes in VANETs. The framework integrates vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data to enhance situational awareness and anomaly detection. By applying supervised and semi-supervised ML models, such as Random Forest and Semi-Supervised Support Vector Machines (S3VM), the system identifies behavioral patterns that indicate malicious activity. Additionally, trust evaluation and data correlation across multiple nodes improve detection accuracy and reduce false positives. Experimental simulations conducted on real-world traffic scenarios demonstrate the robustness and adaptability of the proposed approach, showing significant improvements over traditional rule-based methods. This research contributes to safer and more reliable VANET operations by introducing a scalable and intelligent solution for localizing malicious nodes. Artificial Intelligence and Machine Learning Electrical Engineering VANET malicious node detection machine learning collaborative framework trust management node localization V2V communication V2I systems anomaly detection intelligent transportation systems 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. 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