SWiST DeepNet: Spectral-Temporal Deep Network for Detecting Resonance-based Attacks on MEMS Inertial Sensors

Micro-Electro-Mechanical Systems (MEMS) based inertial sensors, such as accelerometers and gyroscopes, are fundamental to Cyber-Physical Systems (CPS) operation, enabling real-time motion tracking and orientation estimation. However, their susceptibility to resonance-based acoustic interference can lead to false measurements, corrupting sensor outputs and potentially destabilizing the entire system relying on them. To detect such threats, this article proposes a sliding-window-based deep learning framework that uses temporal and spectral-domain features to identify resonance-induced signal injections. The model is evaluated under diverse and realistic operating conditions, including both static and dynamic motion, and across three defined attack levels: soft, medium, and hard. The experimental analysis compares the performance of temporal, spectral, and a fusion of spectral-temporal domain features for attack detection across different window lengths. The proposed Sliding Window Spectral Temporal (SWiST) DeepNet architecture reliably detects attack even at low intensities down to 10 dB Acoustic-to-Signal Ratio, significantly enhancing the resilience and trustworthiness of CPS against resonance-based attacks. Our datasets are available at https://github.com/cmsahu97/RAiS-dataset