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Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

arXiv 2210.02841 ↗First posted 1 Oct 2022cs.CR cs.LGcited by 0

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Abstract (arXiv)

Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. To this end, we propose a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs contrastive learning in an adversarial setup to learn effective representations of normal and anomalous behavior in wireless networks. We conduct rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques and verify that CAAD yields a mean performance improvement of 92.84%. Additionally, we also augment CAAD enabling it to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). We view CAAD-EF as a novel, holistic and widely applicable solution to anomaly detection.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 1 Oct 2022.

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References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2020Supervised Contrastive Learningcited by 2,803
  6. 2017Improved Training of Wasserstein GANscited by 2,223
  7. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  8. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  9. 2017Wasserstein GANcited by 635
  10. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  11. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  12. 2019Deep Anomaly Detection with Deviation Networkscited by 152
7 more
  1. 2021Mean-Shifted Contrastive Loss for Anomaly Detectioncited by 87
  2. 2018Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detectioncited by 79
  3. 2014Toward Supervised Anomaly Detectioncited by 73
  4. 2021Training GANs with Stronger Augmentations via Contrastive Discriminatorcited by 45
  5. 2020Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Datacited by 42
  6. 2021Masked Contrastive Learning for Anomaly Detectioncited by 23
  7. 2020GAN Ensemble for Anomaly Detectioncited by 22

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