Network attack identification effectively secures Internet of Things (IoT) application scenarios. However, dynamic scene changes, attack feature reliance, high data dimensions, and challenges with spatio-temporal feature fusion frequently pose limitations to attack traffic identification in IoT contexts. Definitive intelligent IoT attack identification enables intelligent algorithms to extract attack features for application scenarios with fixed topological environments but cannot construct the intricate changes of IoT application scenarios. Through the dynamic acquisition, feature awareness, and deep learning, intelligent digital twin-based attack detection can address these issues and enhance attack identification for IoT threats. Thus, this paper proposed an intelligent digital twin method based on spatio-temporal feature fusion for IoT attack behavior identification. Firstly, feature subsets are selected based on information gain to reduce the dimensionality of IoT data with high traffic; Secondly, a parallel spatio-temporal feature extraction model is designed unlike the existing tandem model, which uses a simplified Convolutional Neural Networks (CNN) model to learn the spatial features of the attack, a Bi-directional Long Short-Term Memory (BiLSTM) model to learn the temporal features of the attack, an attention mechanism to fuse the temporal and spatial features, and the (Deep Neural Networks) DNN to learn the combined features; Finally, the virtual instance space and topology of the attack scenario are simulated using digital twin (DT) to build a digital version of the complex system for IoT applications and tested in a simulation environment. Based on experimental results using the UNSW-NB15 and CICIDS2017 datasets, this paper shows that the proposed method can extract spatio-temporal features from network attack traffic and has a 5% improvement in test accuracy.

Unmanned Aerial Vehicles (UAVs), which are a crucial element of the future air-space-ground integrated network, can serve as potential mobile edge computing (MEC) nodes due to their onboard capabilities for storage, communication, and computation. However, current UAV-assisted MEC collaborative offloading methods primarily focus on addressing network requirements for computing tasks, while neglecting the heterogeneity in task demands due to the heterogeneity of task types and UAV service types. To address this, we propose a task demand-oriented collaborative offloading and deployment strategy in software-defined UAV-assisted edge networks. Specifically, to enhance the cache utilization of UAVs, we introduce an SD-UEN (Software-Defined UAV-Edge Network) architecture, which facilitates cooperation among UAVs under the guidance of an SDN (Software-Defined Networking) controller. In light of the heterogeneity of task demands, we employ the Tabu Search-based Matching (TSM) algorithm to accurately match computing tasks with the appropriate UAV modes. Furthermore, to enable intelligent UAV mode switching and dynamic UAV location deployment, we leverage the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By centrally training the MADDPG model offline, MEC servers and UAVs, acting as learning agents, can efficiently adjust UAV modes and deploy UAVs during online execution. This algorithm dynamically optimizes UAV actions to minimize task completion time and energy consumption. The simulation results highlight that our algorithm substantially reduces task response time and energy consumption compared to other algorithms, demonstrating its effectiveness.

The novel Vehicular Cloud Network (VCN) is a close combination of Vehicular Ad hoc Network, Cloud Computing, and Edge Computing. However, VCN is facing the enormous challenge of security and privacy before it is generalized. To address these issues, VCN communication characteristics and security requirements are summarized first. Secondly, the paper conducts an in-depth analysis of the existing security problems and solutions in VCN based on several attack models, including routing, data, identity, DoS, and hostile attacks. And we summarize the security solutions into two categories: security defense measures based on identity authentication and access control, and security protection measures based on traffic detection and anomaly detection. Thirdly, this paper proposes a reliable VCN security architecture consisting of three types of clouds: vehicular, edge, and central clouds. Moreover, the VCN architecture combines a Vehicle Intrusion Detection and Prevention System with a Vehicular Cloud Security Management Platform to ensure the security of VCN. Last but not least, some open issues worth investigating are discussed.

Deep learning has proven to be effective for enhancing the accuracy and efficiency of attack detection through training with large sample sizes. However, when applied to cyber–physical systems (CPSs), it still encounters challenges such as scarcity of attack samples, the difficulty of selecting features for high-dimensional data, and weak model-generalization ability. In response, this paper proposes ResADM, a transfer-learning-based attack detection method for CPSs. Firstly, an intentional sampling method was employed to construct different sets of samples for each class, effectively balancing the distribution of CPS-attack samples. Secondly, a feature-selection method based on importance was designed to extract the meaningful features from attack behaviors. Finally, a transfer-learning network structure based on ResNet was constructed, and the training parameters of the source model were optimized to form the network-attack detection method. The experimental results demonstrated that ResADM effectively balanced the data classes and extracted 32-dimensional attack-behavior features. After pre-training on the UNSW-NB15 dataset, ResADM achieved a detection accuracy of up to 99.95% for attack behavior on the CICIDS2017 dataset, showcasing its strong practicality and feasibility.

With the advancement of artificial intelligence (AI) technology, edge networks are progressively evolving toward intelligence, and mobile intelligent agents such as uncrewed aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) are playing an increasingly vital role in this process. However, existing studies have addressed cooperation among homogeneous agents, such as multiple UAVs, without exploring collaboration between heterogeneous mobile intelligent agents. For this purpose, we propose a vehicle-mounted multi-UAV cooperative service system to collaboratively collect and process multisource heterogeneous Internet of Things (IoTs) tasks under strict latency and resource constraints. To address the complexities of heterogeneous task structures and dynamic collaboration, the article first introduces a multisource heterogeneous task scheduling mechanism, which optimizes task prioritization and resource allocation for efficient processing. In addition, a decentralized reinforcement learning approach based on partial reward decoupling (PRD) heterogeneous agent proximal policy optimization (PRD-HAPPO) is employed to enhance collaboration and trajectory planning between UAVs and vehicles. Simulation results demonstrate that the proposed framework significantly improves task completion efficiency, reduces system latency, and outperforms existing deep reinforcement learning (DRL) algorithms in terms of convergence and scalability.