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Cited by 27Network Security and Intrusion DetectionAnomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

A novel anomaly detection approach based on ensemble semi-supervised active learning (ADESSA)

Niu, Zequn, Guo, Wenjie, Xue, Jingfeng, Wang, Yong, Kong, Zixiao, Huang, Lu·2023·Computers & Security
DOI 10.1016/j.cose.2023.103190
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Crossref · 10.1016/j.cose.2023.103190

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  • ADESSA achieves stable attack/normal classification under limited labeling budgets and class imbalance in CPS networks by iteratively incorporating a small number of manually labeled high-confidence samples with automatically labeled samples.

    “By iteratively incorporating a small number of manually labeled high-confidence samples with automatically labeled samples, the method achieves stable attack/normal classification under limited labeling budgets and class imbalance in CPS networks.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [34]

  • Niu et al. [34] proposed ADESSA, an ensemble semi-supervised active-learning-based method using flow-level and statistical features from NSL-KDD and SWaT datasets.

    “Niu et al. [34] proposed ADESSA, an ensemble semi-supervised active-learning-based method using flow-level and statistical features from NSL-KDD and SWaT datasets.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [34]

  • ADESSA employs an ensemble of Random Forest, Gradient Boosted Trees, and Extra Trees and constructs a balanced training set through a balanced sampling strategy combining margin sampling and democratic co-learning.

    “The model employs an ensemble of Random Forest, Gradient Boosted Trees, and Extra Trees, and constructs a balanced training set through a balanced sampling strategy that combines margin sampling and democratic co-learning.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [34]

  • Several works attempt to bridge the gap between network statistics and process context by combining network statistics with temporal models or with process-oriented datasets such as SWaT, WADI, BATADAL, and Gas Pipeline, but a full characterization of process-level anomalies still requires complementary process or hybrid data.

    “Several works attempt to bridge this gap by combining network statistics with temporal models or with process-oriented datasets such as SWaT, WADI, BATADAL, and Gas Pipeline [23,25,29,34], but a full characterization of process-level anomalies still requires complementary process or hybrid data, which are analyzed in later subsections.”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [34]

  • Supervised statistical classifiers tend to achieve high accuracy when ample labeled attack data are available, whereas unsupervised and semi-supervised methods offer better robustness to previously unseen or zero-day attacks at the expense of slightly lower nominal accuracy.

    “Supervised statistical classifiers tend to achieve high accuracy when ample labeled attack data are available [13,15,17,20,23,25,32,34], whereas unsupervised and semi-supervised methods offer better robustness to previously unseen or zero-day attacks at the expense of slightly lower nominal accuracy [17,20,21,25,28,34].”

    From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [34]

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Authors
Niu, Z., Guo, W., Xue, J., Wang, Y., Kong, Z., & Huang, L.
Affiliations
Beijing Institute of Technology (CN)
Corresponding author
Wang, Yong
ORCID
Venue
Computers & Security
Publisher
Elsevier BV
Published
2023-06
Volume
129
Pages
103190
ISSN
0167-4048 (ISSN-L)
DOI
10.1016/j.cose.2023.103190
OpenAlex
W4328101188
Cited by
27
References
43
Citations by year
2026: 5 · 2025: 12 · 2024: 7 · 2023: 3
FWCI
3.00
Type
journal article
Language
en
Open access
closed
Licence
Locations
Computers & Security (publishedVersion)
Funders
  • National Natural Science Foundation of China (62172042)
  • Major Scientific and Technological Innovation Project of Shandong Province (2020CXGC010116)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
anomaly detection, cyber-physical systems, semi-supervised active learning, ensemble learning, class imbalance, margin sampling, label budget, network traffic anomaly detection, NSL-KDD dataset, SWaT dataset

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