Semi-supervised anomaly detection in dynamic communication networks

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 datasets 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.



Work Title Semi-supervised anomaly detection in dynamic communication networks
Open Access
  1. Xuying Meng
  2. Suhang Wang
  3. Zhimin Liang
  4. Di Yao
  5. Jihua Zhou
  6. Yujun Zhang
  1. Anomaly detection
  2. Semi-supervised learning
  3. Generative adversarial networks
  4. Self-learning
License In Copyright (Rights Reserved)
Work Type Article
  1. Information Sciences
Publication Date June 9, 2021
Publisher Identifier (DOI)
Deposited July 25, 2022




This resource is currently not in any collection.

Work History

Version 1

  • Created
  • Added 1-s2.0-S0020025521003856-main.pdf
  • Added Creator Xuying Meng
  • Added Creator Suhang Wang
  • Added Creator Zhimin Liang
  • Added Creator Di Yao
  • Added Creator Jihua Zhou
  • Added Creator Yujun Zhang
  • Published
  • Updated Keyword, Publication Date Show Changes
    • Anomaly detection, Semi-supervised learning, Generative adversarial networks, Self-learning
    Publication Date
    • 2021-09-01
    • 2021-06-09
  • Updated