DSTAN-Med uses separate sensor-wise and time-wise attention plus a zero-parameter physiological filter to detect falsified vital signs, reporting 7.4-8.3 percentage point sensitivity gains over Transformer baselines on three public datasets.
Anomaly-based intrusion detection for IoMT networks: De- sign, implementation, dataset generation, and ML algorithms evalua- tion,
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DSTAN-Med: Dual-Channel Spatiotemporal Attention with Physiological Plausibility Filtering for False Data Injection Attack Detection in IoT-Based Medical Devices
DSTAN-Med uses separate sensor-wise and time-wise attention plus a zero-parameter physiological filter to detect falsified vital signs, reporting 7.4-8.3 percentage point sensitivity gains over Transformer baselines on three public datasets.