mlsanityData to 7 Oct 2026

IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT

arXiv 1611.04880 ↗First posted 15 Nov 2016cs.CRcited by 30

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Abstract (arXiv)

With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IOT SENTINEL is effective in identifying device types and has minimal performance overhead.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Nov 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 1; 2023: 9; 2024: 6; 2025: 4; 2026: 4.

30 papers.

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  1. 2021Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Surveycited by 9
  2. 2021Revisiting IoT Device Identificationcited by 6
  3. 2021Exploring Edge TPU for Network Intrusion Detection in IoTcited by 5
  4. 2021IoTDevID: A Behavior-Based Device Identification Method for the IoTcited by 4
  5. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
  6. 2023ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classificationcited by 3
  7. 2023Enhancing IoT Security via Automatic Network Traffic Analysis: The Transition from Machine Learning to Deep Learningcited by 2
  8. 2023IoT Device Identification Based on Network Communication Analysis Using Deep Learningcited by 1
  9. 2023A Novel IoT Trust Model Leveraging Fully Distributed Behavioral Fingerprinting and Secure Delegationcited by 1
  10. 2025What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scalecited by 1
  11. 2026NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled Evasioncited by 1
  12. 2021Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learningcited by 0
18 more
  1. 2021Collaborative adversary nodes learning on the logs of IoT devices in an IoT networkcited by 0
  2. 2022AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networkscited by 0
  3. 2023Internet of Things: Digital Footprints Carry A Device Identitycited by 0
  4. 2023Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networkscited by 0
  5. 2023CNN-based IoT Device Identification: A Comparative Study on Payload vs. Fingerprintcited by 0
  6. 2023LSTM based IoT Device Identificationcited by 0
  7. 2023IoTFlowGenerator: Crafting Synthetic IoT Device Traffic Flows for Cyber Deceptioncited by 0
  8. 2024Locality Sensitive Hashing for Network Traffic Fingerprintingcited by 0
  9. 2024Communication Traffic Characteristics Reveal an IoT Devices Identitycited by 0
  10. 2024Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Surveycited by 0
  11. 2024Leveraging Machine Learning for Accurate IoT Device Identification in Dynamic Wireless Contextscited by 0
  12. 2024GeMID: Generalizable Models for IoT Device Identificationcited by 0
  13. 2024Improving the network traffic classification using the Packet Vision approachcited by 0
  14. 2025Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streamscited by 0
  15. 2025REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitationscited by 0
  16. 2026IoT Device Identification with Machine Learning: Common Pitfalls and Best Practicescited by 0
  17. 2026From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signalscited by 0
  18. 2026Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labelingcited by 0