基于注意力机制的GRU神经网络安全态势预测方法

传统的网络安全态势预测方法依赖于历史态势值的准确性,并且各种网络安全因素之间存在相关性和重要程度差异性.针对以上问题,提出一种基于注意力机制的循环门控单元(recurrent gate unit,GRU)编码预测方法,该方法利用GRU神经网络挖掘网络安全态势数据之间的时间相关性;引入注意力机制计算安全指标的分配权重并将...

Security situation prediction method of GRU neural network

Traditional network security situation prediction methods rely on the accuracy of the historical situation value, and there are differences in the correlation and importance between various network security factors. For the above mentioned problems, the recurrent gated unit (GRU) coding prediction method based on the attention mechanism is proposed. Attention mechanism is introduced to calculate the weight of the security index and it is coded as the network security situation value. The improved particle swarm optimization (PSO) algorithm is used to optimize the super parameters to accelerate the training of GRU neural network. Simulation results show that the proposed method has faster convergence speed and lower complexity, smaller mean square error (MSE) and mean absolute error (MAE) under different prediction time.