SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction
Some low-cost Internet of Things (IoT) sensor deployments lack device-level source authentication, leaving them vulnerable to impersonation or injected sensor readings. We present a lightweight approach to sensor impersonation detection in a small proof-of-concept study. We formulate detection as a sequence-prediction problem. A model with three LSTM layers and two fully connected layers is trained only on univariate temperature readings from a genuine sensor, and a window of readings is flagged when its mean absolute prediction error exceeds the mean genuine error by more than six standard deviations. The model is converted to TensorFlow Lite in three variants, non-quantized (554 KB), 16...
| Authors | Nahom Birhan |
|---|---|
| Primary category | cs.CR |
| Published | 5 September 2026 |
| Last updated at source | 11 September 2026 |
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