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It is crucial to ensure secure communication among entities in the VANET for realizing an efficient transportation system. In this scenario, the current communication scheme is vulnerable to the leakage of private information from entities. The research primarily centers on single\u2010domain vehicular networks, with only a limited number of researchers exploring cross\u2010domain authentication among vehicle entities. Cross\u2010domain communication schemes have received little attention from scholars. Furthermore, there are issues, including the susceptibility of in\u2010vehicle conversations to eavesdropping, the vulnerability of long\u2010distance transmissions to interruptions, and the exposure of wireless networks to traffic attacks. To address these issues, a multidomain secure communication and intelligent traffic detection model in VANET is proposed. This model offers several notable advantages as follows: (1) It employs a key self\u2010verification algorithm for local computation and authentication of entity keys. This approach mitigates the risks of identity impersonation attacks and key leakage results from third\u2010party key escrow. (2) A multidomain communication scheme is devised to categorize vehicle\u2010to\u2010vehicle (V2V) scenarios into intradomain and interdomain, which correspond to situations where the communicating parties are within the same domain and across different domains, respectively. (3) We propose the implementation of new session message encryption algorithms for V2V communication. This involves generating dynamic random keys to ensure secure data sharing and facilitates long\u2010distance cross\u2010domain communication among vehicles. (4) An intelligent two\u2010layer traffic detection paradigm is proposed to improve the efficiency of detecting attack traffic in vehicular networks. This paper provides security proofs and performance analysis of the proposed scheme. The experimental results demonstrate that within the communication module, the comparative scheme exhibits high computational demands and significant delays, whereas our approach provides superior security and better computational performance. Compared to the traditional detection model, our two\u2010layer detection paradigm reduces model training time by 69\u20134477\u2009ms and testing time by 9\u20131469\u2009ms.<\/jats:p>","DOI":"10.1155\/int\/2539516","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T02:35:38Z","timestamp":1748572538000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multidomain Secure Communication and Intelligent Traffic Detection Model in 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