Intertwining smart manufacturing and the Internet of Things (IoT) is known as the Industrial Internet of Things (IIoT). IIoT improves product quality and reliability and requires intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. Recently, it has been increasingly deployed; however, multi-party collaborative information processing is often required in heterogeneous IIoT. The security and efficiency requirements of each party interacting with other partners have become a significant challenge in information security. This paper proposes an automated smart manufacturing framework based on Digital Twin (DT) and Blockchain. The data used in the DT are all from the cluster generated after blockchain authentication. The processed data in the DT will only be accessed and visualized in the cloud when necessary. Therefore, all the data transmitted in the process are result reports, avoiding the frequent transmission of sensitive data. Simulation results show that the proposed authentication mode takes less time than the standard protocol. In addition, our DT framework for a smart factory deploys the PDQN DRL model, proving to have higher accuracy, stability, and reliability.

Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the remaining challenges for securing V2X communications in the face of evolving threats.

Vehicle-to-everything (V2X) communications enable vehicles to interact with each other and various components of the traffic system, forming the backbone of modern intelligent transportation systems. However, V2X communications are highly susceptible to cyberattacks, posing a threat to both safety and operational efficiency. This paper proposes a real-time anomaly detection framework that integrates the reconstruction capabilities of Variational Autoencoders (VAEs) with the feature extraction power of Convolutional Neural Networks (CNNs). Our model processes streaming data using a sliding window mechanism, ensuring prompt detection of anomalies in the dynamic V2X environment. Extensive experiments demonstrate that our method achieves high performance across diverse anomaly types, with precision, recall, and F1-scores reaching up to 0.91, 0.99, and 0.95, respectively, on challenging anomalies such as constant position offsets. The model consistently outperforms both a traditional autoencoder and a VAE with Long Short-Term Memory (LSTM) layers, particularly on complex anomalies like vehicle speed and position offsets. Additionally, our framework maintains a low inference time of approximately 0.0013 s, making it highly suitable for real-time deployment. Designed to adapt to evolving traffic patterns through periodic retraining, the proposed approach ensures long-term reliability and robustness. By delivering high performance, adaptability, and efficiency, our method provides a reliable way to detect and prevent cyberattacks, thereby making intelligent transportation systems safer and more dependable.