A Network Security Situation Prediction Based on Empirical Mode Decomposition and Improved Temporal Transformer
Aimed at the problems that the network security situation prediction task is complex,and high in noise of data in real environments,a network security situation prediction method is proposed based on empirical mode decomposition(EMD)and improved temporal Transformer(ITTransformer).The com-plete EEMD with adaptive noise(CEEMDAN)method is utilized for de-noising and pre-processing net-work security situation data in real environments through"decomposition-reconstruction".The paper pro-poses ITTransformer.The Temporal Transformer module is used to extract the time-depth global features from the network security situation data sequences.An Attention Fusion mechanism is proposed to realize the adaptive fusion of temporal features to complete the prediction task in a more robust feature fusion way.The experimental results show that the method proposed in this paper is superior in prediction accu-racy to the other methods,and its coefficient of determination reaches 0.997 860,and the fitting efficiency is good.
network security situation predictiontime series decompositionTransformerfeature fusionattention mechanism
万方数据
维普
