期刊
INFORMATION SCIENCES
卷 571, 期 -, 页码 527-542出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2021.04.056
关键词
Anomaly detection; Semi-supervised learning; Generative adversarial networks; Self-learning
资金
- National Key Research and Development Program of China [2018YFB1800403]
- National Science Foundation of China [61902382, 61972381, 61672500]
- Strategic Priority Research Program of Chinese Academy of Sciences [XDC02030500]
- research program of Network Computing Innovation Research Institute [E061010003]
To address security concerns in communication networks, a semi-supervised anomaly detection framework called SemiADC is proposed, which improves the accuracy of anomaly detection through self-learning processes.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据