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

TL;DR

DSTAN-Med introduces a novel dual-channel attention framework with physiological plausibility filtering to detect false data injection attacks in IoMT sensor streams, significantly improving detection sensitivity.

Contribution

It proposes a new supervised deep learning framework with orthogonal attention pathways and domain-knowledge-based filtering for enhanced attack detection in medical IoT data.

Findings

Achieves 7.4-8.3% higher sensitivity over Transformer baselines.

Physiological plausibility filter improves precision by 3.1-4.2% without sensitivity loss.

Each component of DSTAN-Med is essential, as shown by ablation studies.

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.