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BoAu: Malicious traffic detection with noise labels based on boundary augmentation

文献类型：期刊论文

作者Yuan, Qingjun1,5; Liu, Chang2,3; Yu, Wentao1,4; Zhu, Yuefei1,5; Xiong, Gang2,3; Wang, Yongjuan1,5; Gou, Gaopeng2,3
刊名COMPUTERS & SECURITY
出版日期2023-08-01
卷号131页码:13
关键词Malicious traffic detection Deep learning Learning with noise labels Decision boundaries Encrypted traffic
ISSN号0167-4048
DOI10.1016/j.cose.2023.103300
英文摘要The effectiveness of deep-learning-based malicious traffic detection systems relies on high-quality labeled traffic datasets. However, malicious traffic labeling approaches can easily lead to incorrect labeling, which can have a harmful impact on models. To this end, various methods for learning with noise labels have been proposed. They exclude suspected wrong samples from model updates to ensure accuracy. However, this also removes hard samples, resulting in poor model decision boundaries and the loss of ability to classify hard samples. In this paper, we propose a boundary-augmentation-based approach for malicious traffic identification named BoAu. Unlike other approaches, BoAu treats all samples, including hard sam-ples, equally during training to construct more accurate decision boundaries and thus improve accuracy. Meanwhile, a decision boundary augmentation module is designed to mitigate the impact of mislabeled hard samples on decision boundary generation. The decision boundary augmentation module adaptively adjusts the losses of hard samples based on their distance from the cluster to which their labels belong and other clusters, thus driving the shared feature representation network to fit the true label distribu-tion. We validated BoAu in identifying malicious traffic with noise labels on a dataset covering 22 classes of realistic encrypted malicious traffic. Experimental results showed that even under scenarios with up to 90% noise labels, the classification accuracy was still over 80%, which was better than the state-of-the-art approaches. In addition, we validated the applicability of BoAu on several public datasets, including CIC-IDS-2017 and IoT-23.& COPY; 2023 Published by Elsevier Ltd.
资助项目National Key Research and Development Program of China[2021YFB3101400] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDC02040400]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001012905000001
出版者ELSEVIER ADVANCED TECHNOLOGY
源URL[http://119.78.100.204/handle/2XEOYT63/21231]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Gou, Gaopeng
作者单位1.Strateg Support Force Informat Engn Univ, Zhengzhou 450001, Peoples R China
2.Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China
3.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100093, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
5.Henan Key Lab Network Cryptog Technol, Zhengzhou 450001, Peoples R China
推荐引用方式
GB/T 7714		 Yuan, Qingjun,Liu, Chang,Yu, Wentao,et al. BoAu: Malicious traffic detection with noise labels based on boundary augmentation[J]. COMPUTERS & SECURITY,2023,131:13.
APA Yuan, Qingjun.,Liu, Chang.,Yu, Wentao.,Zhu, Yuefei.,Xiong, Gang.,...&Gou, Gaopeng.(2023).BoAu: Malicious traffic detection with noise labels based on boundary augmentation.COMPUTERS & SECURITY,131,13.
MLA Yuan, Qingjun,et al."BoAu: Malicious traffic detection with noise labels based on boundary augmentation".COMPUTERS & SECURITY 131(2023):13.

入库方式： OAI收割

来源：计算技术研究所

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