{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:17:49Z","timestamp":1781018269498,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,26]],"date-time":"2023-02-26T00:00:00Z","timestamp":1677369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Norwegian University of Science","award":["PNURSP2023R323"],"award-info":[{"award-number":["PNURSP2023R323"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network\u2019s performance has improved because training and testing time do not increase when we include more nodes in the network.<\/jats:p>","DOI":"10.3390\/s23052594","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T02:15:37Z","timestamp":1677464137000},"page":"2594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":95,"title":["An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs)"],"prefix":"10.3390","volume":"23","author":[{"given":"Kanwal","family":"Rashid","sequence":"first","affiliation":[{"name":"Department of IT, The University of Haripur, Haripur 22620, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yousaf","family":"Saeed","sequence":"additional","affiliation":[{"name":"Department of IT, The University of Haripur, Haripur 22620, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2917-6598","authenticated-orcid":false,"given":"Abid","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering and Technology, Taxila 54000, Pakistan"},{"name":"Department of Computer Science, GANK(S) DC KTS Haripur, Haripur 22620, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal","family":"Jamil","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Larsg\u00e5rdsvegen 2, 6009 Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6395-4723","authenticated-orcid":false,"given":"Reem","family":"Alkanhel","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0213-8145","authenticated-orcid":false,"given":"Ammar","family":"Muthanna","sequence":"additional","affiliation":[{"name":"Department of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 Saint Petersburg, Russia"},{"name":"Department of Applied Probability and Informatics, Peoples\u2019 Friendship University of Russia (RUDN University), 117198 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Al-Omaisi, H., Sundararajan, E.A., and Abdullah, N.F. (2019, January 9\u201310). Towards vanet-ndn: A framework for an efficient data dissemination design scheme. 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