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SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction
Date pendingNahom BirhanInternet Of Thing Sensors
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Date pendingXixian Liao, Maite MeleroTranslation Quality Teacher
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Date pendingValerio Franchi, Rafael Garcia, Nuno Gracias +7Skin Lesion Classification Microscopy
QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language
Date pendingNiloy Kumar Mondal, Md Rizwan ParvezQuantum Approximate Optimization Algorithm Bilevel Optimization
ActMap: Single-Pass Uncertainty Quantification from Generation-Time Activation Maps
Date pendingJacopo Dardini (University of Bologna), Roberta Calegari (University of Bologna)Large Language Model Uncertainty Etsi-Compliant Class Activation Map

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SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction

Nahom Birhan

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-bit weight quantized (298.5 KB), and 8-bit weight quantized (185 KB), targeting an Arduino Nano 33 BLE aggregator. The variants were evaluated with the TensorFlow Lite interpreter on the local server, since on-device execution of LSTM models was not yet supported by TensorFlow Lite for Microcontrollers at the time of the study. On a controlled testbed with the impostor sensor placed in a hotter outdoor location, the three variants reached detection accuracies of 99.980%, 99.972%, and 98.206%, and each flagged the change point when a test sequence switched from genuine to impostor data. Quantization made the model smaller but slower in our measurements. The genuine and impostor distributions were well separated, so these results show detection of a controlled distribution shift and should not be read as evidence of general device authentication.

Internet Of Thing Sensors Authentication Cortex Sequence Modeling Mean Squared Error Edge

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