mlsanityData to 14 Oct 2026

GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

arXiv 1903.06661 ↗First posted 15 Mar 2019cs.LG stat.MLcited by 36

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Abstract (arXiv)

This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and machine learning techniques in a supervised way, such solutions have the limitations that they require large amount of labeled data for training and are unlikely to detect zero-day attacks. Existing anomaly detection solutions also do not provide an easy way to explain or identify attacks in the anomalous traffic. To address these limitations, we develop and present GEE, a framework for detecting and explaining anomalies in network traffic. GEE comprises of two components: (i) Variational Autoencoder (VAE) - an unsupervised deep-learning technique for detecting anomalies, and (ii) a gradient-based fingerprinting technique for explaining anomalies. Evaluation of GEE on the recent UGR dataset demonstrates that our approach is effective in detecting different anomalies as well as identifying fingerprints that are good representations of these various attacks.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Mar 2019.

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Citing papers per year: 2021: 7; 2022: 13; 2023: 7; 2024: 5; 2025: 2; 2026: 2.

36 papers.

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  1. 2022A Survey on Explainable Anomaly Detectioncited by 39
  2. 2021One-Class Classification: A Surveycited by 35
  3. 2022Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgottencited by 25
  4. 2022Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunitiescited by 25
  5. 2021Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)cited by 15
  6. 2021Explainable Machine Learning for Fraud Detectioncited by 9
  7. 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 9
  8. 2022Machine Learning in NextG Networks via Generative Adversarial Networkscited by 8
  9. 2022Trustworthy Anomaly Detection: A Surveycited by 5
  10. 2022Framing Algorithmic Recourse for Anomaly Detectioncited by 5
  11. 2023Robust online active learningcited by 5
  12. 2021A new interpretable unsupervised anomaly detection method based on residual explanationcited by 4
24 more
  1. 2022Explaining Anomalies using Denoising Autoencoders for Financial Tabular Datacited by 4
  2. 2022Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanationscited by 2
  3. 2023MixedTeacher : Knowledge Distillation for fast inference textural anomaly detectioncited by 2
  4. 2023Targeted collapse regularized autoencoder for anomaly detection: black hole at the centercited by 2
  5. 2021Robustness of ML-Enhanced IDS to Stealthy Adversariescited by 1
  6. 2022VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surfacecited by 1
  7. 2022ENCODE: Encoding NetFlows for Network Anomaly Detectioncited by 1
  8. 2023FABLE : Fabric Anomaly Detection Automation Processcited by 1
  9. 2024Transfer learning applications for anomaly detection in wind turbinescited by 1
  10. 2024Improved Anomaly Detection through Conditional Latent Space VAE Ensemblescited by 1
  11. 2025UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Securitycited by 1
  12. 2025A systematic literature review of unsupervised learning algorithms for anomalous traffic detection based on flowscited by 1
  13. 2021Improving Botnet Detection with Recurrent Neural Network and Transfer Learningcited by 0
  14. 2021Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learningcited by 0
  15. 2022Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detectioncited by 0
  16. 2022Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spacescited by 0
  17. 2023Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systemscited by 0
  18. 2023Detection of Sensor-To-Sensor Variations using Explainable AIcited by 0
  19. 2023Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detectioncited by 0
  20. 2024CSE: Surface Anomaly Detection with Contrastively Selected Embeddingcited by 0
  21. 2024Spectral Image Data Fusion for Multisource Data Augmentationcited by 0
  22. 2024State Frequency Estimation for Anomaly Detectioncited by 0
  23. 2026PLAA: Packet-level Adversarial Attacks in Network Traffic Detectioncited by 0
  24. 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Surveycited by 0