Network security situation assessment and prediction method based on multimodal transformation in edge computing

Network security play a critical role in ensuring the robustness and integrity of network systems. We propose a novel method based on multimodal transformation in edge computing for accurate and reliable data communication. The method leverages Graph Convolutional Networks (GCNs) to capture and analyze the complex relationships and dependencies among network entities, enabling enhanced prediction capabilities. By integrating multimodal transformation, diverse data sources are fused in the model. The assessments and comprehensive comparisons with established algorithms unequivocally establish the supremacy of our proposed approach, particularly with respect to metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Our method exhibits promising performance, enabling proactive threat detection and mitigation in network security by more then 90% accuracy. In the future work, we have included the different multimodal transformation techniques, addressing interoperability challenges, scaling the method for large-scale networks, and adapting the model to dynamic network environments.