Semi-supervised anomaly detection in dynamic communication networks

doi:10.1016/j.ins.2021.04.056

	Semi-supervised anomaly detection in dynamic communication networks
	Meng, Xuying 1,2; Wang, Suhang 3; Liang, Zhimin 1; Yao, Di 1; Zhou, Jihua 4; Zhang, Yujun 1
	2021-09-01
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
卷号	571 页码:527-542
摘要	To ensure the security and stabilization of the communication networks, anomaly detection is the first line of defense. However, their learning process suffers two major issues: (1) inadequate labels: there are many different kinds of attacks but rare abnormal nodes in mt of these atstacks; and (2) inaccurate labels: considering the heavy network flows and new emerging attacks, providing accurate labels for all nodes is very expensive. The inadequate and inaccurate label problem challenges many existing methods because the majority normal nodes result in a biased classifier while the noisy labels will further degrade the performance of the classifier. To tackle these issues, we propose SemiADC, a Semi-supervised Anomaly Detection framework for dynamic Communication networks. SemiADC first approximately learns the feature distribution of normal nodes with regularization from abnormal ones. It then cleans the datasets and extracts the nodes sasainaccurate labels by the learned feature distribution and structure-based temporal correlations. These self-learning processes run iteratively with mutual promotion, and finally help increase the accuracy of anomaly detection. Experimental evaluations on real-world data sets demonstrate the effectiveness of our SemiADC, which performs substantially better than the state-of-art anomaly detection approaches without the demand of adequate and accurate supervision. (c) 2021 Published by Elsevier Inc.
关键词	Anomaly detection Semi-supervised learning Generative adversarial networks Self-learning
DOI	10.1016/j.ins.2021.04.056
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1800403] ; National Science Foundation of China[61902382] ; National Science Foundation of China[61972381] ; National Science Foundation of China[61672500] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDC02030500] ; research program of Network Computing Innovation Research Institute[E061010003]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000683548900009
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17232
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Yujun
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Purple Mt Labs, Nanjing, Peoples R China 3.Penn State Univ, Coll Informat Sci & Technol, State Coll, PA USA 4.Jin Mei Commun, Chogqing, Peoples R China
推荐引用方式 GB/T 7714	Meng, Xuying,Wang, Suhang,Liang, Zhimin,et al. Semi-supervised anomaly detection in dynamic communication networks[J]. INFORMATION SCIENCES,2021,571:527-542.
APA	Meng, Xuying,Wang, Suhang,Liang, Zhimin,Yao, Di,Zhou, Jihua,&Zhang, Yujun.(2021).Semi-supervised anomaly detection in dynamic communication networks.INFORMATION SCIENCES,571,527-542.
MLA	Meng, Xuying,et al."Semi-supervised anomaly detection in dynamic communication networks".INFORMATION SCIENCES 571(2021):527-542.