中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Semi-supervised anomaly detection in dynamic communication networks

文献类型：期刊论文

作者Meng, Xuying1,2; Wang, Suhang3; Liang, Zhimin1; Yao, Di1; Zhou, Jihua4; Zhang, Yujun1
刊名INFORMATION SCIENCES
出版日期2021-09-01
卷号571页码:527-542
关键词Anomaly detection Semi-supervised learning Generative adversarial networks Self-learning
ISSN号0020-0255
DOI10.1016/j.ins.2021.04.056
英文摘要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.
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
语种英语
WOS记录号WOS:000683548900009
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/17231]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。