融合神经网络和优化算法的网络安全态势评估及预测模型研究

Research on Network Security Situation Assessment and Prediction Model Based on the Fusion of Neural Network and Optimization Algorithm

针对网络安全防护,提出一种融合神经网络与优化算法的态势评估模型。利用深度神经网络进行网络态势评估模型构建,并通过长短时记忆构建网络态势预测模型,利用遗传算法实现模型参数调优。结果表明,评估模型的平均误差率相比于浅层神经网络模型降低了3.88%。因此,研究设计的基于优化深层神经网络与长短时记忆网络的态势感知模型,具有较好的评估预测性能。

A situation assessment model is proposed to address the issue of network security protection by combining neural network and optimization algorithm.It utilizes deep neural networks to construct a network situation assessment model,and construct a network situation prediction model through long short-term memory.The genetic algorithm is used to optimize model parameters.The results show that the average error rate of the assessment model is reduced by 3.88% comparing with the shallow neural network model.Therefore,the situation awareness model designed based on optimized deep neural networks and long short-term memory networks has good assessment and prediction performance.