SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction

• URL: http://arxiv.org/abs/2609.06271v2
• Date: Fri, 11 Sep 2026 02:28:53 GMT
• Title: SIDE: Sensor Impersonation Detection at the Edge via Sequence Prediction
• Abstract summary: Some low-cost Internet of Things (IoT) sensor deployments lack device-level source authentication.<n>We present a lightweight approach to sensor impersonation detection in a small proof-of-concept study.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks [0.0]
  Postprocessing routines encode assumptions that no longer hold on utility-scale devices.<n>We present a case study of textttSamplerQNN, the sampling-based quantum neural network class in the Qiskit Machine Learning library.<n>On our quantum hardware runs, where bit-strings span over 100 physical qubits, this filter led to the loss of 85 to 99.6% of valid measurement shots.
  arXiv  Detail & Related papers  (2026-09-04T12:22:13Z)
• Level, Sharpness, and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer [36.02174685737611]
  We find that detector rankings reverse across deployments, every detector exceeds $80%$ FPR95 on at least one domain, and the preferred detector depends on both the in-distribution data and the underlying VLM.<n>We introduce the Complementary Evidence Guard (CEG), a detector-agnostic wrapper that preserves complementary evidence through a non-compensatory fusion of the base detector, level, and sharpness.
  arXiv  Detail & Related papers  (2026-07-29T08:00:49Z)
• Heralded Leakage Detection with Preserved Computational-State Coherence in a Fixed-Frequency Transmon [7.0825911951696625]
  Leakage out of the computational subspace is a major error mechanism in superconducting quantum processors.<n>We demonstrate heralded leakage detection on a fixed-frequency transmon by applying a Rabi drive on the computational transition during dispersive readout.
  arXiv  Detail & Related papers  (2026-07-19T11:52:17Z)
• Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data [0.0]
  Event detection underpins induced-seismicity monitoring for CCS and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring.<n>We assemble a unified binary event-detection benchmark from three physically distinct real sources.<n>We evaluate a fault-tolerant detector trained with per-sample sensor-dropout against standard detectors.
  arXiv  Detail & Related papers  (2026-06-28T11:14:20Z)
• Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification [4.2958409172092225]
  Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream.<n>Recent European requirements, including ETSI TS 119 461 and CEN/TS 18099, motivate complementary evidence channels beyond camera-based presentation-attack detection.<n>This paper investigates whether passive motion traces recorded during selfie capture provide auxiliary evidence for spoof screening and user verification.
  arXiv  Detail & Related papers  (2026-04-30T20:46:18Z)
• WISV: Wireless-Informed Semantic Verification for Distributed Speculative Decoding in Device-Edge LLM Inference [56.297697169678095]
  WISV (Wireless-Informed Semantic Verification) is a novel distributed speculative decoding framework.<n>We show that WISV achieves up to a 60.8% increase in accepted length, a 37.3% reduction in interaction rounds, and a 31.4% improvement in end-to-end latency.<n>We validate WISV on a hardware testbed comprising an NVIDIA Jetson AGX Orin and an A40-equipped server.
  arXiv  Detail & Related papers  (2026-04-20T01:29:56Z)
• Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data [0.13750624267664155]
  This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller.<n>Anomalies are detected using lightweight Z-Score thresholds, enabling interpretable and computationally efficient inference suitable for embedded deployment.<n>Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB and 63 KB Flash.
  arXiv  Detail & Related papers  (2026-03-28T20:15:50Z)
• Adaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection [0.5284217353503208]
  We motivate and describe Adaptive Label Error Detection (ALED), a novel method of detecting mislabeling.<n>ALED has markedly increased sensitivity, without compromising precision, compared to established label error detection methods.<n>We demonstrate an example where fine-tuning a neural network on corrected data results in a 33.8% decrease in test set errors.
  arXiv  Detail & Related papers  (2026-01-15T05:20:00Z)
• Fast Re-Trainable Attention Autoencoder for Liquid Sensor Anomaly Detection at the Edge [0.0]
  A lightweight, edge-deployable pipeline is proposed for detecting sensor anomalies in chemistry and biology laboratories.<n>A custom PCB captures seven sensor channels and streams them over the local network.<n>An Attention-based One-Class Autoencoder reaches a usable state after training on only thirty minutes of normal data.
  arXiv  Detail & Related papers  (2025-07-05T10:51:58Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Fault-tolerant parity readout on a shuttling-based trapped-ion quantum computer [64.47265213752996]
  We experimentally demonstrate a fault-tolerant weight-4 parity check measurement scheme. We achieve a flag-conditioned parity measurement single-shot fidelity of 93.2(2)%. The scheme is an essential building block in a broad class of stabilizer quantum error correction protocols.
  arXiv  Detail & Related papers  (2021-07-13T20:08:04Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Object Detection Made Simpler by Eliminating Heuristic NMS [70.93004137521946]
  We show a simple NMS-free, end-to-end object detection framework. We attain on par or even improved detection accuracy compared with the original one-stage detector.
  arXiv  Detail & Related papers  (2021-01-28T02:38:29Z)
• Probing Model Signal-Awareness via Prediction-Preserving Input Minimization [67.62847721118142]
  We evaluate models' ability to capture the correct vulnerability signals to produce their predictions. We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR) The results show a sharp drop in the model's Recall from the high 90s to sub-60s with the new metric.
  arXiv  Detail & Related papers  (2020-11-25T20:05:23Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection [99.94079901071163]
  This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs) We target applications where multiple sensors transmit data to a local processing unit, which executes a detection algorithm. Our detector is able to perform a detection every 450 ms, with an overall testing F1 score of 83%.
  arXiv  Detail & Related papers  (2020-04-03T17:36:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.