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D"IoT: A Federated Self-learning Anomaly Detection System for IoT

arXiv 1804.07474 ↗First posted 20 Apr 2018cs.CRcited by 89

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Abstract (arXiv)

IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present D"IoT, an autonomous self-learning distributed system for detecting compromised IoT devices effectively. In contrast to prior work, D"IoT uses a novel self-learning approach to classify devices into device types and build normal communication profiles for each of these that can subsequently be used to detect anomalous deviations in communication patterns. D"IoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, D"IoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that D"IoT is highly effective (95.6% detection rate) and fast (~257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. D"IoT reported no false alarms when evaluated in a real-world smart home deployment setting.

Key facts. Cited by 89 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2018.

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Citing papers per year: 2021: 19; 2022: 21; 2023: 14; 2024: 11; 2025: 11; 2026: 13.

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  1. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  2. 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 78
  3. 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
  4. 2021Federated Learning for Malware Detection in IoT Devicescited by 36
  5. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  6. 2022Machine Learning for Microcontroller-Class Hardware: A Reviewcited by 28
  7. 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
  8. 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 26
  9. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learningcited by 25
  10. 2023BayBFed: Bayesian Backdoor Defense for Federated Learningcited by 23
  11. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
  12. 2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
38 more
  1. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  2. 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
  3. 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challengescited by 16
  4. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
  5. 2021Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacycited by 14
  6. 2021Federated Variational Learning for Anomaly Detection in Multivariate Time Seriescited by 12
  7. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  8. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  9. 2022Fast Deep Autoencoder for Federated learningcited by 10
  10. 2024Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailabilitycited by 9
  11. 2021Decentralized Federated Learning Preserves Model and Data Privacycited by 8
  12. 2023BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Liftingcited by 7
  13. 2021Revisiting IoT Device Identificationcited by 6
  14. 2022Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensemblingcited by 6
  15. 2022FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systemscited by 6
  16. 2023ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attackscited by 6
  17. 2021Active Learning for Network Traffic Classification: A Technical Studycited by 5
  18. 2022Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensorscited by 5
  19. 2022Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networkscited by 5
  20. 2022An Interpretable Federated Learning-based Network Intrusion Detection Frameworkcited by 4
  21. 2022A review of Federated Learning in Intrusion Detection Systems for IoTcited by 4
  22. 2023FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attackscited by 4
  23. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  24. 2024Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generatorcited by 4
  25. 2022Deep Federated Anomaly Detection for Multivariate Time Series Datacited by 3
  26. 2022CELEST: Federated Learning for Globally Coordinated Threat Detectioncited by 3
  27. 2023Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Thingscited by 3
  28. 2024Federated PCA on Grassmann Manifold for IoT Anomaly Detectioncited by 3
  29. 2025Quantifying the Privacy Implications of High-Fidelity Synthetic Network Trafficcited by 3
  30. 2021FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learningcited by 2
  31. 2021Robust Federated Learning for execution time-based device model identification under label-flipping attackcited by 2
  32. 2021Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networkscited by 2
  33. 2022GowFed -- A novel Federated Network Intrusion Detection Systemcited by 2
  34. 2023Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detectioncited by 2
  35. 2024On the Convergence Rates of Federated Q-Learning across Heterogeneous Environmentscited by 2
  36. 2024FedMSE: Semi-supervised federated learning approach for IoT network intrusion detectioncited by 2
  37. 2021SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systemscited by 1
  38. 2021FedDICE: A ransomware spread detection in a distributed integrated clinical environment using federated learning and SDN based mitigationcited by 1

50 most cited of 89 All 89 (JSON)