%0 Journal Article
%A CHEN Shuang-wu
%A CHEN Xiang
%A YANG Jian
%A PENG Yu-he
%T Cross-Domain Abnormal Traffic Detection Based on Transfer Learning
%D 2021
%R 10.13190/j.jbupt.2020-114
%J Journal of Beijing University of Posts and Telecommunications
%P 33-39
%V 44
%N 2
%X In order to solve the problem that the machine learning model based on known data is not completely reliable in actual abnormal traffic detection tasks due to the dynamics of the network environment. The different distributed traffic as the source domain and target domain is used to establish a cross-domain framework for abnormal network traffic detection. The transfer learning method based on joint distribution adaptation is proposed by finding the optimal transformation matrix, adapting the conditional probability and edge probability between the source domain and the target domain, the feature transfer between the source domain and the target domain is realized thereby for solving the problem of the large difference in the distribution of the source domain and the target domain causes problems such as decreased detection accuracy. Experiments show that the proposed method can significantly improve the detection accuracy of cross-domain traffic.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2020-114