Abstract: In order to improve the accuracy, stability, and robustness of network security situation assessment, a network security situation assessment model based on improved selective kernel convolutional neural network and support vector machine is proposed. Firstly,the traditional kernel for feature extraction is replaced with the improved selective kernel to enhance the adaptability of the convolutional neural network to changes in receptive field,thereby strengthening the correlation between features. Then, the extracted features are fed into the support vector machine for classification, and the grid optimization algorithm is used to optimize the parameters in the support vector machine globally. Finally, the network security situation value is calculated according to the network attack impact index.Experimental results show that the situation assessment model based on improved selective kernel convolutional neural network and support vector machine achieves higher accuracy,stronger stability and robustness compared to traditional convolutional neural networks.

Key words: network security situation assessment, network security situation awareness, improved selective kernel convolutional neural network, support vector machine, grid optimization algorithm