Adversarial Machine Learning (AML), particularly model poisoning, presents a critical threat to Autonomous Vehicles (AVs) in the Internet of Vehicles (IoV) environment. To address this challenge, we propose a framework that integrates Federated Learning (FL) with a Deep Learning-based Intrusion Detection and Behavior Monitoring (DL-based IBM) system, a vehicle scoring mechanism, Digital Twin (DT), and Generative AI (Gen-AI) to enhance security in IoV environments. Each AV employs a DL-based IBM as its local model, which is trained on vehicle-local operational data and contributes to a global model through FL aggregation. A vehicle scoring system is responsible for identifying and flagging compromised AVs (Zombies) exhibiting suspicious behavior. When the DT detects that the Zombie’s model has been manipulated through poisoning attacks, the DT updates the compromised model with the correct global model to restore normal operation. Furthermore, DT leverages Gen-AI to simulate and learn from novel attack scenarios that AVs have not previously encountered, ensuring that the framework adapts to evolving threats. The simulation results demonstrate significant improvements in resilience and detection accuracy, with F1 scores reaching 99% for known attacks and exceeding 87% for new unseen threats. This integrated approach ensures robust and adaptive protection for AVs, maintaining high trust and performance in the dynamic and adversarial IoV network.

Edge computing improves the Internet of Vehicles (IoV) by offloading heavy computations from in-vehicle devices to high-capacity edge servers, typically roadside units (RSUs), to ensure rapid response times for intensive and latency-sensitive tasks. However, maintaining Quality of Service (QoS) remains challenging in dense urban settings and remote areas with limited infrastructure. To address this, we propose an software-defined networking (SDN)-driven model for uncrewed aerial vehicle (UAV)-assisted vehicular edge computing (VEC), integrating RSUs and UAVs to provide computing services and gather global network data via an SDN controller. UAVs serve as adaptable platforms for mobile-edge computing (MEC), filling gaps left by traditional MEC frameworks in areas with high vehicle density or sparse network resources. An optimal offloading mechanism, designed to minimize the Age of Information (AoI) while balancing energy consumption and rental costs, is implemented through a soft actor-critic (SAC)-based algorithm that jointly optimizes UAV trajectory, user association, and offloading decisions. Experimental results demonstrate the model’s superior performance, achieving up to 87.2% energy savings in energy-limited settings and a 50% reduction in time-sensitive scenarios, consistently outperforming traditional strategies across various task sizes.

Due to the extremely important role of data in the industrial Internet of Things (IIoT) network, trust and security of data are among the major concerns. In this article, we develop a cloud-integrated 5G-IIoT network architecture enabled by a three-party authenticated key exchange (AKE) protocol with privacy-preserving to secure data exchanged via wireless communication, cope with unauthorized entities, and ensure data integrity. Moreover, we develop a trust model based on the Dempster–Shafer theory to check the trustworthiness of data collected by smart devices/sensor nodes. Security analysis performed on our scheme demonstrates that it can withstand different well known attacks in the IIoT environment. We also analyzed the validity of our scheme by using the automated validation of internet security protocols and applications tool. Additionally, the performance evaluation and experimental results prove the effectiveness of the proposed scheme compared to the existing works in terms of accuracy, delay, trust, and throughput.

In real-time medical monitoring systems, given the significance of medical data and disease symptoms, a secure and always-on connection with the medical centre over the public channels is essential. To this end, an edge-enabled Internet of Medical Things (IoMT) scheme is designed to improve flexibility and scalability of the network and provide seamless connectivity with minimum latency. The entities involved in such network are vulnerable to various attacks and can potentially be compromised. To address this issue, an authentication scheme comprised of digital signature and Authenticated Key Exchange (AKE) protocol is proposed which guarantees only authorized entities get access to the services available in the medical system. Moreover, to fulfill the privacy-preserving, each entity is mapped to a different pseudo-identity. The non-mathematical and performance analysis show that the proposed scheme is robust against various attacks such as impersonation and replay attacks.

Intensity Particle Flow (IPF) SMC-PHD has been proposed recently for multi-target tracking. In this paper, we extend IPF-SMC-PHD filter to distributed setting, and develop a novel consensus method for fusing the estimates from individual sensors, based on Arithmetic Average (AA) fusion. Different from conventional AA method which may be degraded when unreliable estimates are presented, we develop a novel arithmetic consensus method to fuse estimates from each individual IPF-SMC-PHD filter with partial consensus. The proposed method contains a scheme for evaluating the reliability of the sensor nodes and preventing unreliable sensor information to be used in fusion and communication in sensor network, which help improve fusion accuracy and reduce sensor communication costs. Numerical simulations are performed to demonstrate the advantages of the proposed algorithm over the uncooperative IPF-SMC-PHD and distributed particle-PHD with AA fusion.

Security and privacy of data-in-transit are critical issues in Industry 4.0, which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this article, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. In addition, we conduct a security analysis that proves that the scheme resists several attacks in the Industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works.

In vehicular ad hoc networks, autonomous vehicles generate a large amount of data prior to support in-vehicle applications. So, big storage and high computation platform are needed. On the other hand, the computation for vehicular networks at the cloud platform requires low latency. Applying edge computation (EC) as a new computing paradigm has potentials to provide computation services while reducing the latency and improving the total utility. We propose a three-tier EC framework to set the elastic calculating processing capacity and dynamic route calculation to suitable edge servers for real-time vehicle monitoring. This framework includes the cloud computation layer, EC layer, and device layer. The formulation of the resource allocation approach is similar to an optimization problem. We design a new reinforcement learning (RL) algorithm to deal with the resource allocation problem assisted by cloud computation. By integration of EC and software-defined networking (SDN), this study provides a new SDN edge (SDNE) framework for resource assignment in vehicular networks. The novelty of this work is to design a multiagent RL-based approach using experience reply. The proposed algorithm stores the users' communication information and the network tracks' state in real time. The results of simulation with various system factors are presented to display the efficiency of the suggested framework. We present results with a real-world case study.