Abstract:

The traditional methods for assessment of network security situation rely on manual label and evaluation.When faced with a large amount of data,there appearsome problems such as low efficiency and poor flexibility.First,we propose a Deep Autoencoder-Deep Neural Network (DAEDNN) model to identify all kinds of attacks on the network.Then,the Under-Over Sampling Weighted (UOSW) algorithm is designed to improve the detection rate of the model on categories with a few training samples.Finally,we conduct model testing and calculate the attack probability.Besides,we determine the impact score of each type of attack and calculate the network security situation value.Experimental results show that the precision and recall of the proposed model are better than those of the compared models,and that the proposed model has a better performance in accuracy and efficiency.

Key words: network security situation assessment, network attacks, deep learning, deep autoencoder, data resampling