In recent years, significant progress has been made in the application of federated learning (FL) in various aspects of cyberspace security, such as intrusion detection, privacy protection, and anomaly detection. However, the robustness of federated learning in the face of malicious attacks (such us adversarial attacks, backdoor attacks, and poisoning attacks) is weak, and the unfair allocation of resources leads to slow convergence and inefficient communication efficiency regarding FL models. Additionally, the scarcity of malicious samples during FL model training and the heterogeneity of data result in a lack of personalization in FL models. These challenges pose significant obstacles to the application of federated learning in the field of cyberspace security. To address these issues, the introduction of meta-learning into federated learning has been proposed, resulting in the development of federated meta-learning models. These models aim to train personalized models for each client, reducing performance discrepancies across different clients and enhancing model fairness. In order to advance research on federated meta-learning and its applications in the field of cyberspace security, this paper first introduces the algorithms of federated meta-learning. Based on different usage principles, these algorithms are categorized into client-level personalization algorithms, network algorithms, prediction algorithms, and recommendation algorithms, and are thoroughly presented and analyzed. Subsequently, the paper divides current cyberspace security issues in the network domain into three branches: information content security, network security, and information system security. For each branch, the application research methods and achievements of federated meta-learning are elucidated and compared, highlighting the advantages and disadvantages of federated meta-learning in addressing different cyberspace security issues. Finally, the paper concludes with an outlook on the deep application of federated meta-learning in the field of cyberspace security.

With the continuous development of autonomous driving, real-time target detection has become increasingly critical in autonomous driving systems. However, traditional target detection algorithms usually require huge computational resources, limiting their application on embedded autonomous driving platforms. To address this challenge, a lightweight single-stage target detection algorithm is proposed YOLOdrive. The inverted residual structure, linear bottleneck layer, and depth-separable convolution in MobileNetv2 are utilized to improve the YOLOv8 backbone network, while the spatial channel reconstructed convolution is used to improve the C2f module of YOLOv8, and the convolution of YOLOv8 neck and detection head is replaced by the depth-separable convolution. Experimental results verify that the average accuracy of YOLOdrive algorithm on MS COCO2017 dataset and VOC2007 dataset is improved compared with the baseline model YOLOv8n. The amount of model parameters has been reduced by more than 50%, and the amount of computation in the model has been reduced by more than 70%. The algorithm drastically reduces the amount of parameters and computational complexity of the network, improves the operational efficiency, saves the storage space of the network, and maintains a high detection performance.

Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities.

The surface texture of objects in industrial scenes is complex and diverse, and the characteristics of surface defects are often very similar to the surrounding environment and texture background, so it is difficult to accurately detect the defect area. However, when deep learning technology is used to detect complex texture surface defects, the detection accuracy is not high, due to the lack of large-scale pixel-level label datasets. Therefore, a defect detection model Siamese-RCNet for complex texture surface with a small number of annotations is proposed. The Cascade R-CNN target detection network is used as the basic framework, making full use of unlabeled image feature information, and fusing the nonlinear relationship learning ability of Siamese network and the feature extraction ability of the Res2Net backbone network to more effectively capture the subtle features of complex texture surface defects. The image difference measurement method is used to calculate the similarity between different images, and the attention module is constructed to weight the feature map of the feature extraction pyramid, so that the model can focus more on the defect area and suppress the influence of complex background texture area, so as to improve the accuracy of detection. To verify the effectiveness of the Siamese-RCNet model, a series of experiments were carried out on the DAGM2007 dataset of weakly supervised learning texture surface defects for industrial optical inspection. The results show that even if only 20% of the labeled datasets are used, the mAP@0.5 of the Siamese-RCNet model can still reach 96.9%. Compared with the traditional Cascade R-CNN and Faster R-CNN target detection networks, the Siamese-RCNet model has high accuracy, can reduce the workload of manual labeling, and provides strong support for practical applications.

Existing network intrusion detection models effectively capture relationships between nodes and extract key features. However, they often struggle to accurately represent node characteristics, particularly in modeling the spatiotemporal dynamics and topological structures with sufficient granularity. To address these limitations, we propose the dynamic interaction multi-channel graph convolutional network (DIMK-GCN), which integrates three key components: a spatiotemporal feature weighting module, an interactive graph feature fusion module, and a temporal feature learning module. The spatiotemporal feature weighting module constructs a dynamic graph structure that incorporates both nodes and edges, leveraging self-attention mechanisms to enhance critical feature representations. The interactive graph feature fusion module employs graph attention networks (GATs) to refine node relationships while integrating a multi-channel graph convolutional network (GCN) to extract multi-perspective features, thereby enhancing model depth and robustness. The temporal feature learning module utilizes gated recurrent units (GRUs) to effectively capture long-term dependencies and address challenges posed by non-stationary time series data. Experimental results on the CIC-IDS2017, CIC-IDS2018, and Edge-IIoTSet datasets demonstrate that DIMK-GCN significantly outperforms existing models in key performance metrics, including detection accuracy, recall, and F1-score. Notably, on the Edge-IIoTSet dataset, DIMK-GCN achieves an accuracy of 97.31%, verifying its effectiveness and robustness in detecting various types of network attacks.

Aiming at the fact that the moving target defense game model fails to accurately portray attack and defense gains, resulting in bias in attack and defense games and the inability to select effective defense strategies, we construct the moving target three-way evolutionary game defense model (MTTEGDM). Firstly, the model is defined and analyzed theoretically under the premise of uncertainty and irrationality. Then, combined with the three-way decisions, the attack intention is introduced into the target network loss calculation, and a dynamic weight adjustment algorithm based on the three-way decisions is proposed to accurately characterize the attack and defense gains from a multi-attribute perspective. Finally, the evolutionary game model is used to analyze the evolution trend of the multi-stage defense strategy, so as to carry out feasible and effective defense behavior. The simulation results show that the model can accurately predict the optimal defense strategy of moving targets in different stages. Through a Monte Carlo simulation experiment, the proposed algorithm is compared with the traditional evolutionary game model, and the effectiveness and security of the proposed algorithm are verified.