Paper 2026/1929

ASCON-Based Lightweight Cryptographic Provenance for Distinguishing Physiological Events, Sensor Faults, and Cyberattacks in IoMT

Abstract

Continuous monitoring in the Internet of Medical Things (IoMT) creates an attribution problem: an abnormal sample may rep- resent a genuine physiological event, an accidental sensor fault, or a deliberate cyberattack. Conventional anomaly detectors estimate whether data are unusual, but cannot establish who produced a sample, which software processed it, or whether it was replayed. This paper proposes Verifiable Medical Sensor Provenance (VMSP), a lightweight chain of authenticated records spanning sensing, gateway processing, and clinical ingestion. We implemented Ascon-AEAD128 and Ascon-Hash256 record pro- tection, converted verification outcomes into machine-learning features, and evaluated 12,000 generated windows. Macro-F1 increased from 0.644 for signal-only classification and 0.807 with device health to 0.976 with Ascon-derived provenance; attack recall increased from 0.398 to 1.000. For a 64-byte payload, the prototype added 68 bytes and measured median encryption and verification times of 0.282 and 0.297 ms on an x86-64 host. These measurements establish a reproducible software proof of concept, not clinical or embedded-device performance.