DSTAN-Med: Dual-Channel Spatiotemporal Attention with Physiological Plausibility Filtering for False Data Injection Attack Detection in IoT-Based Medical Devices

• URL: http://arxiv.org/abs/2605.14165v1
• Date: Wed, 13 May 2026 22:39:47 GMT
• Title: DSTAN-Med: Dual-Channel Spatiotemporal Attention with Physiological Plausibility Filtering for False Data Injection Attack Detection in IoT-Based Medical Devices
• Abstract summary: False data injection (FDI) attacks on Internet of Medical Things (IoMT) sensor streams falsify vital signs in transit.<n>Existing deep learning detectors conflate inter-sensor spatial correlations with temporal dependencies in a shared latent space.<n>We propose DSTAN-Med, a supervised framework that routes multivariate sensor windows through independent sensor-wise (SWA) and time-wise (TWA) self-attention pathways.
• Score: 1.1459450491541954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: False data injection (FDI) attacks on Internet of Medical Things (IoMT) sensor streams falsify vital signs in transit, threatening patient safety and defeating clinical monitoring systems that lack cyber-physical anomaly detection capability. Existing deep learning detectors conflate inter-sensor spatial correlations with temporal dependencies in a shared latent space, preventing disentanglement of the distinct spatial and temporal signatures that FDI attacks imprint simultaneously; no current method exploits domain knowledge to constrain outputs against physiologically impossible attack patterns. We propose DSTAN-Med, a supervised framework comprising a Dual-channel Attention Mechanism (DAM) that routes multivariate sensor windows through independent sensor-wise (SWA) and time-wise (TWA) self-attention pathways operating on orthogonal tensor axes, a residual 1D-CNN block for local temporal feature extraction, and a zero-parameter Physiological Plausibility Filter (PPF) that suppresses attack signatures violating domain-knowledge bounds. Evaluated across three IoMT sensor datasets - PhysioNet/CinC 2012 (ICU vital signs), MIMIC-III Waveform (continuous ICU waveforms), and WESAD (wearable biosensor signals) - DSTAN-Med achieves mean sensitivity gains of 7.4-8.3 percentage points over the strongest Transformer baseline (TranAD), with improvements significant at p < 0.01 (McNemar's test, Holm-Bonferroni correction). The PPF contributes independent precision gains of 3.1-4.2 percentage points at negligible sensitivity cost across all three corpora. Ablation studies confirm that each component is individually necessary; removal of residual connections alone reduces sensitivity by 14.0 percentage points. The source code is publicly available at https://github.com/mehedi93hasan/DSTAN-MED.

Related papers

• IoMT-SecAlarmBench: A Counterfactual Benchmark for Integrity Attacks in IoMT [0.0]
  Internet of Medical Things (IoMT) combines clinical physiological data with cyber-system information, creating challenges in determining whether an abnormal reading reflects a genuine physiological event, a device fault, or a cyber-attack within the expected physiological range.<n>We present IoMT-SecAlarmBench, a semi-synthetic benchmark that injects controlled integrity attacks into genuine ECG+ recordings using a structured experimental design combining four attack morphologies, four severity levels, two physiological plausibility conditions, and replay attacks.<n>Results show no method consistently detects the most difficult cases: replay attacks and low-amplitude transient spikes remain close to
  arXiv  Detail & Related papers  (2026-08-26T21:29:26Z)
• Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments [40.05961815531593]
  Internet of Things (IoT) applications generate vast amounts of Correlated Time Series (CTS) data that often contain missing values and require imputation.<n>We propose AdaCTSi, an adaptive CTS imputer for changing environments.<n>A single trained model supports sensor-subset and resource-adaptive inference, and its modest memory footprint enables deployment on commodity computing devices.
  arXiv  Detail & Related papers  (2026-07-26T07:08:00Z)
• Leveraging Imperfect Medical Data: A Manifold-Consistent Spatio-Temporal Network for Sensor-based Human Activity Recognition [7.630146071495278]
  In real-world wearable sensing scenarios, IoMT signals are often corrupted by missing measurements, sensor failures, and environmental noise.<n>We propose a Manifold-Consistent Spatio-Temporal Network (MCSTN) for robust HAR under imperfect sensing conditions.<n>We show that the proposed MCSTN achieves competitive performance compared with existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-04-29T10:55:41Z)
• Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions [59.49967357689445]
  Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies.<n>This study presents a complete open-source dilated real-based framework for the development and validation of deep learning models for affective touch recognition in soft interactive companions.
  arXiv  Detail & Related papers  (2026-04-16T10:20:05Z)
• Using Unsupervised Domain Adaptation Semantic Segmentation for Pulmonary Embolism Detection in Computed Tomography Pulmonary Angiogram (CTPA) Images [0.0]
  unsupervised domain adaptation (UDA) framework is proposed, utilizing a Transformer backbone and a Mean-Teacher architecture for cross-center semantic segmentation.<n>The primary focus is placed on enhancing pseudo-label reliability by learning deep structural information within the feature space.<n> Experimental validation conducted on cross-center datasets (FUMPE and CAD-PE) demonstrates significant performance gains.
  arXiv  Detail & Related papers  (2026-02-23T14:33:24Z)
• Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters [39.9470953186283]
  Continuous electrocardiogram (ECG) monitoring via wearables offers significant potential for early cardiovascular disease (CVD) detection.<n> deploying deep learning models for automated analysis in resource-constrained environments faces reliability challenges due to Out-of-Distribution data.<n>This paper explores Unsupervised Anomaly Detection (UAD) as an independent, upstream filtering mechanism to improve robustness.
  arXiv  Detail & Related papers  (2025-10-30T13:54:37Z)
• Prototyping an End-to-End Multi-Modal Tiny-CNN for Cardiovascular Sensor Patches [35.899401205042615]
  We propose a convolutional neural network with early fusion of data to solve a binary classification problem.<n>Our approach reduces memory footprint and compute cost by three orders of magnitude compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2025-10-21T14:23:20Z)
• Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications [58.796149594878585]
  This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks.<n>In the considered system, a central reader node actively queries a subset of neuromorphic sensor nodes (neuro-SNs) at each time frame.<n>The neuromorphic sensors are event-driven, producing spikes in correspondence to relevant changes in the monitored system.
  arXiv  Detail & Related papers  (2025-10-16T13:56:54Z)
• Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior [4.432163893362497]
  We introduce a paradigm that first clusters MEG channels based on their sensor's spatial position.<n>Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals.<n>Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%.
  arXiv  Detail & Related papers  (2025-01-05T02:06:13Z)
• Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors [6.369424231505372]
  Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease.<n>We present FOGSense, a real-world deployable FOG detection system using only a single sensor.
  arXiv  Detail & Related papers  (2024-11-18T17:43:43Z)
• Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization [42.14439854721613]
  Jamming devices disrupt signals from the global navigation satellite system (GNSS)<n>This paper introduces an extensive dataset comprising snapshots obtained from a low-frequency antenna.<n>Our objective is to assess the resilience of machine learning (ML) models against environmental changes.
  arXiv  Detail & Related papers  (2024-09-23T15:20:33Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization [52.82138296332476]
  This paper makes a first attempt to formulate cross-domain Dysarthric speech detection (DSD) as an unsupervised domain adaptation problem. We propose a multi-task learning strategy, including dysarthria presence classification (DPC), domain adversarial training ( DAT) and mutual information minimization (MIM) Experiments show that the incorporation of UDA attains absolute increases of 22.2% and 20.0% respectively in utterance-level weighted average recall and speaker-level accuracy.
  arXiv  Detail & Related papers  (2021-06-18T13:34:36Z)
• SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching [47.79483848496141]
  We propose a 3D sphere representation-based center-points matching detection network (SCPM-Net) It is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters. We show that our proposed SCPM-Net framework achieves superior performance compared with existing used anchor-based and anchor-free methods for lung nodule detection.
  arXiv  Detail & Related papers  (2021-04-12T05:51:29Z)

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.