With the rapid development of the Internet of Things (IoT)-based near-Earth remote sensing technology, the problem of network intrusion for near-Earth remote sensing systems has become more complex and large-scale. Therefore, seeking an intelligent, automated, and robust network intrusion detection method is essential. Many researchers have researched network intrusion detection methods, such as traditional feature-based and machine learning methods. In recent years, network intrusion detection methods based on graph neural networks (GNNs) have been proposed. However, there are still some practical issues with these methods. For example, they have not taken into consideration the characteristics of near-Earth remote sensing systems, the state of the nodes, and the temporal features. Therefore, this article analyzes the factors of existing near-Earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes and applies them to the network intrusion detection of near-Earth remote sensing systems. Finally, the proposed method in this article is validated using the latest flow-based datasets NF-BoT-IoT-v2 and NF-ToN-IoT-v2. The results demonstrate that the binary classification accuracy for network intrusion detection exceeds 99%, while the multi-classification accuracy exceeds 93%. These findings provide substantial evidence that the proposed method outperforms existing intrusion detection techniques.

The Internet of Vehicles (IoV) has a typical Internet of things architecture, which is the specific application of Internet of things (IoT) technology in the field of intelligent transportation. Virtual network embedding (VNE) is the effective allocation of physical network resources to the virtual network, and is a virtualization technology widely used in the data center of the IoV. The existing VNE algorithm ignores the attributes of the network itself. It only allocates resources according to the order in which the requests arrive, resulting in the problem of low utilization of physical resources. In this paper, we propose an efficient topology control scheme for mobile nodes in IoVs. We firstly present a novel hierarchy-based dynamic VNE model for IoV. Then a VNE algorithm based on hierarchical priority is established by considering the attributes of virtual network requests. Finally, a global resource capacity based dynamic VNE algorithm is designed. Compared with precious VNE algorithms, the proposed algorithm has a 19 percent to 23 percent increase in the resource utilization rate of nodes, and elevates the overall request acceptance rate and revenue overhead, and can reduce the average waiting time of virtual network requests.

With the increasing scale of parallel computer interconnection network, the possibility of processor failure or link failure between processors in the network is also increasing. In the design of supercomputers, not only link overhead and communication delay should be taken into account, but also fault-tolerant performance of networks should be emphasized. Locally exchanged twisted cube (LeTQ) is a newly proposed interconnection network with lower link overhead and shorter diameter. With the increasing scale of supercomputers, fault-tolerant routing is indispensable. In this paper, we propose a new load balancing fault-tolerant routing algorithm based on node contraction for LeTQ networks. The proposed algorithm uses the node shrinkage method to evaluate the priority of nodes. The sending node adaptively adjusts the probability of forwarding packets to the neighbor node according to the priority of the neighbor node and the state of the network. The path can be adapted to the load state of the network. The simulation results show that the fault-tolerant routing algorithm has good performance in throughput and delay.

The rapid growth and increasing complexity of Internet of Things (IoT) devices have made network intrusion detection a critical challenge, especially in ed

Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively.