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.

Traditionally, industrial control systems operate in isolated networks with proprietary solutions. As smart factories and digital twins have become inevitable with AI advancement, the rapid adoption of Industrial Internet of Things (IIoT) devices has significantly increased cybersecurity risks. More precisely, the complexity of industrial environments, which includes production processes and device roles, creates substantial challenges for secure deployment. The authors introduce a bottom-up, industrial role-oriented verification framework (IROVF) for manufacturing line deployment. IROVF incorporates SCADA's MTU and RTU components, which are mapped to distinct device roles. This provides authentication and least-privilege principles that are tailored to factory environments. The proposed framework designs an alarm strategy, which can be helpful to detect and report potential operational disruptions during runtime, thus minimizing impact on system availability. Experimental results demonstrate the superior security coverage of the proposed framework compared to existing research, while a comprehensive application scenario validates its practical applicability. The scalable security parameters of IROVF allow organizations to select appropriate security levels based on their specific requirements. IROVF provides an effective security solution for modern industrial control systems during deployment phases.

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.

Traditional industries rapidly transcend the time and place restrictions of the country according to the technology growth by leaps and bounds over the year. Regrettably, international cybercrime incidents simultaneously explode by 2,400 million from 2020 to 2021. Undoubtedly, the real-time incident response has become the primary subject of incident handling. In this article, we aim to propose lawful remote forensics mechanism for ensuring the optimal protection of potential evidence in stochastic and unpredictable transnational crime. Meanwhile, the entire process can be performed remotely and compliant with legal requirements, such as ISO/IEC and NIST regulations. Specifically, all the procedures can be retroactive based on the design of the chain of custody, which leads to the proof of evidence admissibility. Aside from the security essential confirmation by the formal tools Proverif, AVISPA, and Scyther, simulation results have demonstrated that remote forensics can fulfill the legal requirements and perform excellently in various incident scales.

The utilization of cloud and edge computing has become one of the most prevailing resource supply mechanisms. Thousands of enterprise users and Internet of Things (IoT) devices have performed operations through services sold by computing resource providers. So far, the auction match is the main strategy for allocating resources, in which there exists a trusted third party playing a role as a broker to deal with resource allocation requests for both providers and consumers. The main concerns in resource allocation architecture are how and how long to solve the Winner Determination Problem (WDP), which is used to lay out the match outcome. It is not easy for a single broker with limited computing power to generate an optimal solution in a short period since its corresponding time complexity is regarded as the NP-hard problem. Meanwhile, it is hard for people to trust the third party thoroughly. That is, there are three potential issues, including centralization, data security, and untrustworthiness in traditional matching architecture. In particular, the real-time matching cannot be achieved to fulfill users who have urgent needs of computing resources. To solve above issues, we have designed a trustworthy and real-time decentralized computing resource allocation platform based on blockchain and smart contract. In order to optimize the allocation results, we improve the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for miners to reach the consensus mechanism. Experimental results and comparison analysis have demonstrated that potential defects could be addressed in the new method, and the real-time resource allocation can be preserved firmly despite the balance vibration of market supply and demand.