Cross-Domain Abnormal Traffic Detection Based on Transfer Learning

doi:10.13190/j.jbupt.2020-114

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (2): 33-39.doi: 10.13190/j.jbupt.2020-114

• The Special Issue on Future Network Architecture and Key Technologies • Previous Articles Next Articles

Cross-Domain Abnormal Traffic Detection Based on Transfer Learning

PENG Yu-he, CHEN Xiang, CHEN Shuang-wu, YANG Jian

School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China

Received:2020-08-04 Online:2021-04-28 Published:2021-04-28

Abstract

Abstract: 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.

Key words: abnormal traffic detection, cross-domain, transformation, joint distribution adaptation, machine learning

CLC Number:

TP393.08

PENG Yu-he, CHEN Xiang, CHEN Shuang-wu, YANG Jian. Cross-Domain Abnormal Traffic Detection Based on Transfer Learning[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(2): 33-39.

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Cross-Domain Abnormal Traffic Detection Based on Transfer Learning

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