mlsanityData to 7 Oct 2026

Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

arXiv 1802.09089 ↗First posted 25 Feb 2018cs.CR cs.AI cs.LGcited by 121

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Abstract (arXiv)

Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. However, a drawback of neural networks is the amount of resources needed to train them. Many network gateways and routers devices, which could potentially host an NIDS, simply do not have the memory or processing power to train and sometimes even execute such models. More importantly, the existing neural network solutions are trained in a supervised manner. Meaning that an expert must label the network traffic and update the model manually from time to time. In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner. Kitsune's core algorithm (KitNET) uses an ensemble of neural networks called autoencoders to collectively differentiate between normal and abnormal traffic patterns. KitNET is supported by a feature extraction framework which efficiently tracks the patterns of every network channel. Our evaluations show that Kitsune can detect various attacks with a performance comparable to offline anomaly detectors, even on a Raspberry PI. This demonstrates that Kitsune can be a practical and economic NIDS.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 22; 2022: 16; 2023: 18; 2024: 22; 2025: 21; 2026: 22.

121 papers.

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  1. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  2. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  3. 2024Large Language Models for Cyber Security: A Systematic Literature Reviewcited by 65
  4. 2024Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systemscited by 63
  5. 2021The Threat of Offensive AI to Organizationscited by 37
  6. 2021Federated Learning for Malware Detection in IoT Devicescited by 36
  7. 2022The Role of Machine Learning in Cybersecuritycited by 21
  8. 2021Realtime Robust Malicious Traffic Detection via Frequency Domain Analysiscited by 20
  9. 2021DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applicationscited by 18
  10. 2022Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detectioncited by 18
  11. 2023netFound: Principled Design for Network Foundation Modelscited by 17
  12. 2022SoK: The Impact of Unlabelled Data in Cyberthreat Detectioncited by 15
38 more
  1. 2021Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacycited by 14
  2. 2023SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detectioncited by 13
  3. 2021MemStream: Memory-Based Streaming Anomaly Detectioncited by 12
  4. 2021TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attackcited by 11
  5. 2022AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detectioncited by 11
  6. 2025PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environmentscited by 9
  7. 2021Ensemble neuroevolution based approach for multivariate time series anomaly detectioncited by 8
  8. 2021Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Reviewcited by 8
  9. 2023Model Selection of Anomaly Detectors in the Absence of Labeled Validation Datacited by 8
  10. 2023METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detectioncited by 8
  11. 2022DI-NIDS: Domain Invariant Network Intrusion Detection Systemcited by 7
  12. 2022TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systemscited by 7
  13. 2022Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistencycited by 6
  14. 2022Quantized Training of Gradient Boosting Decision Treescited by 6
  15. 2022FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systemscited by 6
  16. 2021Exploring Edge TPU for Network Intrusion Detection in IoTcited by 5
  17. 2021An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attackscited by 5
  18. 2024Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speedcited by 5
  19. 2021Artificial Neural Network for Cybersecurity: A Comprehensive Reviewcited by 4
  20. 2022An Interpretable Federated Learning-based Network Intrusion Detection Frameworkcited by 4
  21. 2024On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detectioncited by 4
  22. 2025Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detectioncited by 4
  23. 2022Early Detection of Network Attacks Using Deep Learningcited by 3
  24. 2021Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Featurescited by 2
  25. 2021Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networkscited by 2
  26. 2021Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detectioncited by 2
  27. 2023A systematic literature review on the code smells datasets and validation mechanismscited by 2
  28. 2023Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detectioncited by 2
  29. 2023Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysiscited by 2
  30. 2023Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changescited by 2
  31. 2024Individual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion Detectioncited by 2
  32. 2024Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approachcited by 2
  33. 2024NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamicscited by 2
  34. 2025Deep Learning-based Intrusion Detection Systems: A Surveycited by 2
  35. 2025Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplanecited by 2
  36. 2025Anomaly Detection in Event-triggered Traffic Time Series via Similarity Learningcited by 2
  37. 2025Demystifying Network Foundation Modelscited by 2
  38. 2021Classifier Calibration: with application to threat scores in cybersecuritycited by 1

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