Vehicular misbehavior detection faces multiple technical challenges, including machine learning-adaptive attacks and trust management issues. A critical concern is the problem of malicious vehicle with high-reputation, in which malicious vehicles exploit trust-based security by maintaining legitimate behavioral profiles while strategically injecting malicious content. This behavior creates systemic vulnerabilities that compromise network trust infrastructures. Attacks affecting high-reputation malicious behavior detection include both external and internal types, requiring holistic defense mechanisms. However, current vehicular security research lacks unified defense against both internal and external attacks. Typically, studies that effectively resist internal attackers demonstrate the weaker defense against external attacks, and vice versa. To mitigate these concerns, we have designed a Training Verdict Autonomous Vehicle Networks architecture (TV-AVN) that develops a novel Verdict Misbehavior Detection System (V-MDS) by combining machine learning with reputation mechanism. The proposed scheme incorporates a public key cryptosystem to enhance security during basic safety message transmission. A local authority regularly consolidates detection outcomes to update vehicle reputation scores. In comprehensive experimental comparisons, our approach demonstrates robust-level security performance, with formal verification tools validating the security robustness of our proposed mechanism. For position falsification attacks, our method achieves average detection performance of 0.99 Precision , 0.98 Recall , and 0.98 F 1- score . Moreover, the proposed approach demonstrates superior resilience against intelligent attacks involving high-reputation attackers. Although the detection performance experiences degradation, our method remains more stable than existing approaches, which suffer rapid deterioration. In summary, TV-AVN establishes reliable communication for vehicle users, maintaining long-term network quality and preserving user confidence in the system.

Zero-trust architecture has received massive attention globally and been a significant development in the field of cybersecurity. Within zero-trust architecture, the continuous authentication (CA) strategy has been proposed to counter the network security threats posed by traditional static authentication mechanisms. However, most studies have focused on either device-to-device authentication or user authentication. This limitation results in risks of identity spoofing or credential theft despite the implementation of the CA mechanism, thus concluding the parity in significance between authenticating users and devices. Furthermore, considering the CA of users, it is essential to face the issue posed by user authentication fatigue. In response to these challenges, this work aims to introduce an unconsciously CA protocol (UCAP) based on zero-trust concepts and behavior biometrics. UCAP utilizes the behavior of keystroke dynamics as a main factor in consistently evaluating the user trust level. This method enables the continual updating of communication keys to preserve robust authentication of both devices and users. The robustness of UCAP has been examined through formal tools, while the experimental outcomes have shown satisfactory performance.