Research on Network Local Security Situation Fusion Method Based on Self-Attention Mechanism
Addressing the issue of traditional network security situation awareness methods being inefficient at integrating multi-node data to obtain a global network security situation,this article proposed a network local security situation fusion method named SA-RBF-CNN,based on self-attention mechanism,radial basis function(RBF)neural network,and convolutional neural network(CNN).Through the self-attention mechanism,the model effectively identifies and emphasizes key nodes,enhancing the understanding of the global security situation.Meanwhile,the improved RBF structure combined with CNN further refines features,boosting the model's ability to capture complex data patterns.Experimental results show that SA-RBF-CNN outperforms other similar methods in key indicators of network security situation prediction.Compared to traditional situation awareness methods,it increases computational speed and reduces communication overhead,proving that the model has certain practical application value.
network security situation awarenessself-attention mechanismdeep learningradial basis function neural network
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