Satellite data is of high importance for ocean environment monitoring and protection. However, due to the missing values in satellite data, caused by various force majeure factors such as cloud cover, bad weather and sensor failure, the quality of satellite data is reduced greatly, which hinders the applications of satellite data in practice. Therefore, a variety of methods have been proposed to conduct missing data imputation for satellite data to improve its quality. However, these methods cannot well learn the short-term temporal dependence and dynamic spatial dependence in satellite data, resulting in bad imputation performance when the data missing rate is large. To address this issue, we propose the Spatio-Temporal Attention Generative Adversarial Network (STA-GAN) for missing value imputation in satellite data. First, we develop the Spatio-Temporal Attention (STA) mechanism based on Graph Attention Network (GAT) to learn features for capturing both short-term temporal dependence and dynamic spatial dependence in satellite data. Then, the learned features from STA are fused to enrich the spatio-temporal information for training the generator and discriminator of STA-GAN. Finally, we use the generated imputation data by the trained generator of STA-GAN to fill the missing values in satellite data. Experimental results on real datasets show that STA-GAN largely outperforms the baseline data imputation methods, especially for filling satellite data with large missing rates.

In the digital era, effective Transaction Fraud Detection (TFD) is essential to ensuring financial security. The considerable class imbalance, with legitimate transactions vastly outnumbering fraudulent ones, presents a significant challenge for TFD models to accurately identify fraudulent patterns. While existing sample-balancing strategies address class imbalance effectively in many contexts, they often fall short in TFD due to fraudsters’ sophisticated concealment tactics, which lead to pronounced behavioral overlap between fraudulent and legitimate transactions. In this paper, we introduce a novel Generative Adversarial Network-based Hybrid Sampling method (GANHS) to effectively address the class imbalance issue. GANHS employs a dual-discriminator generative adversarial network to generate synthetic samples that accurately reflect the characteristics of fraudulent activity, while an adaptive neighborhood-based undersampling technique refines these samples to minimize overlap with legitimate ones. This hybrid approach not only enhances the model’s ability to learn fraud patterns by generating high-quality samples but also improves its resilience against highly concealed fraudulent activities. Experiments on real-world and public datasets demonstrate that GANHS outperforms its competitive peers, with gains of 0.5%–8.7% in average $F_{1}$-Score and 1.0%–7.0% in G-mean, highlighting its strong potential for improving the reliability and effectiveness of TFD systems in complex, high-risk financial scenarios.

The explosive development of the Internet of Things (IOT) has stimulated the sudden and dynamic demand pattern of potential Service Function Chain (SFC) traffic. The virtual network built based on the requirements of Multiple SFCs (Multi-SFCs), sharing the resource, should provide better Quality of Experience (QoE) for Multi-SFCs according to the real-time network status. However, simultaneous Multi-SFCs in the routing process compete for the VNF resources of the same node and preempt the same link. The instability of QoE caused by node invalidation and link faults is highlighted. Therefore, to provide a more efficient and stable service to Multi-SFCs, quantitative QoE utility and stability models and cooperative Multi-SFCs path allocation are needed. Considering the limited resources, this paper designs a stable QoE-aware Multi-SFCs Cooperative Routing Mechanism (CRM) based on Deep Reinforcement Learning (DRL). Firstly, we model the problem of Multi-SFCs cooperative routing aiming at optimizing diverse QoEs of utility, stability and delay, and formulate it as a multi-objective optimization problem. Moreover, the QoE stable queueing model based on node and link availability is formulated by Lyapunov drift, which can potentially mitigate the QoE fluctuations. Then we associate triple Dueling Deep Q-Networks (DDQNs) and propose a cooperative computing multi-task DRL to obtain the optimal path allocation policy. Experiments are conducted to verify the effectiveness and the results show that our method outperforms its peers on QoE, delay, throughput and stability.

Effective Service Function Chains (SFCs) mapping and Virtual Network Functions (VNFs) scheduling are crucial to ensure high-quality service provision for Internet of Things (IoT) tasks. Meeting the varying demands of multiple SFCs poses a significant challenge, particularly when working with the limited resources available in edge computing networks. Most existing working focuses on uniformly mapping and scheduling service requests in a batch processing manner within a given time period, without taking the diversity and priority of VNFs into account. When there is a sudden surge in demand, the issues of VNF queueing waiting and resources imbalance become prominent. To address the mentioned issues, this paper proposes a Deep Reinforcement Learning (DRL)-based VNF cooperative scheduling framework with priority-weighted delay. In light of the urgency of VNFs with higher priorities and the limitations of available resources, we begin by modeling an average queuing delay with priority weight based on the shortest remaining time priority technique. We then formulate a mathematical optimization problem to minimize the modeled delay in VNF scheduling process while providing suitable multidimensional resources in the edge network. Finally, a DRL method with experience replay and target Q-network is designed to effectively obtain the optimal solutions of the optimization problem from experience. The experimental results show that our proposed method outperforms its peers in terms of SFC request acceptance, delay, load balance, and resource utilization.

As consumer electronics increasingly rely on secure digital transactions, ensuring transaction security remains a formidable challenge, largely due to the severe class imbalance between legitimate and fraudulent transactions. Although existing methods attempt to mitigate this imbalance, their effectiveness is often compromised by the sophisticated concealment strategies employed by fraudsters, which lead to significant behavioral overlap between legitimate and fraudulent transactions. In this paper, we propose a novel Time-aware Generative Network (TaGN) that combines a Density-based Wasserstein Generative Adversarial Network (DWGAN) with a Time-aware Gated Recurrent Network (TaGRN) to enhance fraud detection in consumer electronics. The DWGAN penalizes synthetic samples that closely resemble legitimate transactions, focusing the generator on fraudulent characteristics and reducing the impact of behavioral overlap. The TaGRN leverages temporal and contextual transactional information through a gated recurrent structure, transforming original transaction features into new representations that effectively distinguish behavior patterns in overlapping regions. Extensive experiments conducted on real-world and public datasets demonstrate that TaGN significantly outperforms its competitive peers, offering a robust and effective framework for enhancing transaction security in consumer electronics.

With higher peak data rates, enhanced reliability, improved energy efficiency, and reduced radio latency, 6G enables cooperative perception in autonomous vehicle clusters. Existing research mainly focuses on establishing communication-connected and structurally stable clusters, while overlooking how members collaborate in perception. To address this gap, we propose a collaborative perception-based autonomous vehicle cluster modeling method for expressways, leveraging the capabilities of 6G networks. This method facilitates collaborative perception within vehicle clusters through the exchange of sensory information among member vehicles. First, we introduce a perception interaction mechanism among vehicles as a foundation for constructing clusters. We then present a primary vehicle selection method and analyze the cluster’s perception gain, collaborative efficiency, and collaborative reliability. Based on this, we develop a vehicle cluster model and solve it using a genetic algorithm. We present a vehicle cluster formation method that brings together vehicles achieving Pareto optimal solutions through specific interaction rules, thereby forming a cluster. The simulation results demonstrate that the proposed method outperforms existing methods in terms of perception gain (PG), collaborative efficiency (CE), and collaborative reliability (CR).