改进的递归神经网络在网络安全态势监测中的应用

On Application of Improved Recurrent Neural Network in Network Security Situation Monitoring

随着网络规模的扩大,组网方式多样化,网络拓扑架构变得更加复杂,网络中的数据流量大规模迅速上升,导致网络负载增大,网络受到的攻击、故障等突发性安全事件更加严峻.该文利用神经网络处理非线性、复杂性等优势,基于改进的递归神经网络预测网络安全态势,实验结果证明该方法运行效率较高,运行结果与实际值相比,误差较低,精确性较高.

With the expansion and diversification of the network,network topology structure becomes more complex,and the data traffic rises rapidly in the network,which causes the network load increases,attack,fault and other unexpected severe network security events.Neural network to deal with nonlinear,complexity advantage of this paper,network security situation prediction based on improved recursive neural networks,experimental results show that the high efficiency of the method,results are compared with the actual values,low error,high accuracy.