The emergence of Edge Computing has led to an increasingly intricate and widespread issue of network infiltration in Edge Computing devices. Therefore, it is crucial to explore intelligent, automated, and resilient methods for network intrusion detection. Graph neural network-based techniques for network intrusion detection have been proposed by many scholars recently. However, the graph construction methods of these approaches cannot fully adapt to real network intrusion datasets, leading to problems such as overfitting and insufficient graph information mining. Furthermore, the methods employed in the model training phase are relatively limited, failing to consider the characteristic presence of grouping within networks. These shortcomings result in a lack of high accuracy in intrusion detection systems, especially in multi-class classification scenarios. This research suggests a graph neural network technique based on behavior similarity employing a graph attention network (BS-GAT) to handle the aforementioned problem. To address overfitting and inadequate graph information mining, a behavioral similarity-based graph creation method is first presented through an analysis of real datasets. Subsequently, the inclusion of edge behavioral relationship weights into the GAT leverages the relationship between data flow and graph structural information, enhancing the performance of the trained model. In the final stage, experiments were executed using the most recent datasets to assess the effectiveness of the proposed behavior similarity-based graph attention network in network intrusion detection. The findings indicate that the proposed approach is highly effective and significantly outperforms existing solutions. In binary classification, recall, precision, f1-score, and accuracy all exceed 99%. For multi-class performance, the recognition accuracy exceeds 93%, the weighted recall surpasses 91%, and the weighted f1-score exceeds 92%, showing a substantial improvement in multi-class recognition effectiveness.

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

With the rapid development of 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, it is essential to seek an intelligent, automated, and robust network intrusion detection method. 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 characteristics of existing near-earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes. The proposed network applies spatiotemporal graph neural networks to the network intrusion detection of near-earth remote sensing systems and validates the effectiveness of the proposed method on the latest flow-based dataset.