arXiv (All)
open access
Robust Lightweight IoT Device Fingerprinting Under Network Traffic Anomalies
Accurate IoT device identification is essential for network security, yet many existing approaches rely on payload inspection, feature engineering, or long observation windows that limit scalability and often assume stable network conditions despite the inherent variability of real-world IoT traffic
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Robust Lightweight IoT Device Fingerprinting Under Network Traffic Anomalies#1394200
- Adaptive Traffic Camouflage: Causal and Resource-Aware Defense Against IoT Fingerprinting#1062999
- Adaptive Traffic Camouflage: Causal and Resource-Aware Defense Against IoT Fingerprinting#1043029
- Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes#328770
- Lightweight Attestation Techniques for the Industrial Internet of Things#442281
Record · ID 1402739
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive