LAUSR

Recently, the massive increase in network users has dramatically increased network traffic, making it more difficult to maintain network security. The task of network security situation element extraction is to detect and classify network traffic. The detection rate of minority class samples is low in existing network traffic feature extraction classification methods, and most of the network threat data have seen extreme sample imbalance, which further affects the detection accuracy of minority class samples. To solve these problems, this paper proposes a network security situation element extraction method using conditional generative adversarial network (CGAN) and Transformer. Here, CGAN is applied to solve the sample imbalance problem in the data and improve the detection accuracy of minority samples. Transformer, as an effective feature learning method in natural language direction, has excellent long-distance feature extraction ability. By combining CGAN with Transformer, the detection accuracy of network traffic can be effectively improved. Also, validation was performed using the UNSW-NB15 and KDDcup99 datasets. Experimental results demonstrate that the method using a combination of CGAN and Transformer improved the detection rate for minority samples compared with other advanced-feature extraction classification methods, thereby improving the overall accuracy, F1-score, and specificity. The results are 89.38 % and 93.07 %, 89.75 % and 93.68 %, 87.65 % and 98.20 %, respectively.