Network Security Situation Prediction Based on Atom Search Optimized Deep Neural Network
In order to improve the accuracy of network security situation prediction,the deep convolution neural network(CNN)was applied to security situation prediction,and the atomic search algorithm was used to improve the depth con-volution neural network to improve its adaptability in network security situation prediction.first,extracted the characteris-tics of network sample traffic and complete initialization,then established a deep CNN network attack detection model,and optimized the CNN network parameters with atomic search optimization(ASO)algorithm.Through the calculation of atomic fitness,mass and acceleration.The speed and position of the new atom to obtain the highest fitness CNN network parameter atomic individual,and then used the optimal parameters for CNN network attack type detection training to de-termine the type of network attack,and finally calculated the network security state according to the attack type weight and host weight potential prediction value.The experiment proved that the network security situation prediction value ob-tained through ASO-CNN algorithm was of high accuracy and stability when the host weight was reasonably set.
network security situationconvolution neural networkatomic search optimizationnetwork attack type
万方数据
维普
