融合经验模态分解与改进时域Transformer的网络安全态势预测

A Network Security Situation Prediction Based on Empirical Mode Decomposition and Improved Temporal Transformer

针对网络安全态势预测任务复杂且真实环境下数据噪声较大等问题,提出一种融合经验模态分解与改进时域Transformer的网络安全态势预测方法,通过“分解-重构”方式使用完全自适应噪声集合经验模态分解方法对真实环境下网络安全态势数据进行去噪预处理;提出改进时域Transformer,使用时域Transformer模块提取网络安全态势数据序列的时间深层全局特征,并提出Attention Fusion机制实现时序特征的自适应融合,以更加稳健的特征融合方式完成预测任务。实验结果表明,本文提出的方法相较其他方法在预测精度方面具有显著提高,其拟合优度决定系数达到0.997860,拟合效果较好。

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 complete EEMD with adaptive noise(CEEMDAN)method is utilized for de-noising and pre-processing network security situation data in real environments through“decomposition-reconstruction”.The paper proposes 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 accuracy to the other methods,and its coefficient of determination reaches 0.997860,and the fitting efficiency is good.