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. We propose a lightweight IoT device identification approach based on packet headers and locality-sensitive hashing (LSH), generating compact fingerprint representations at packet- and window-level granularities. To evaluate robustness, we consider packet-level traffic anomalies including drop, perturbation, reordering, duplication, and packet injection. Experimental results show that the prop...
| Authors | Roya Taheri, Shamik Sengupta |
|---|---|
| Primary category | cs.NI |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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