Previous studies have demonstrated that inherent imperfections in inertial sensors can serve as unique identifiers for devices, supporting secure key generation and device authentication. However, these works have primarily focused on measurements taken under static conditions, where sensors were at rest. This paper extends that line of research by investigating inertial sensor-based device identification under dynamic conditions. Specifically, when devices are in motion within a vehicle. We demonstrate that despite motion-induced variations, consistent inter-sensor relationships can be exploited to accurately identify individual devices. Furthermore, we present an ICMetric key generation approach that leverages accelerometer and gyroscope differential readings, using derived alignment and granularity parameters to dynamically regenerate cryptographic keys without storing them. Using five Raspberry Pi 5 devices, each with four MPU-9250 sensors, our experiments demonstrate effective device identification and assess the resilience of motion-regenerated ICMetric-based keys to brute-force attacks.

This paper presents an open-source benchmark tool for anomaly detection in program behaviour, using program counter (PC) and instruction type information. It is introducing anomalies in artificial way, allowing for fine-grained evaluation with adjustable sliding window sizes and preprocessing configuration. The usage of the benchmark, including demonstrated data collection, does not require any additional hardware other than a standard computer. The benchmark uses the output of llvm-objdump program to focus on non-library code which allows for rapid evaluation of various detection methods with different configurations. The proposed tool extracts features derived from processor’s PC and instruction type information and then utilizes the features to identify abnormal behavior using 4 different anomaly detection algorithms. New detection methods can be easily incorporated into the benchmark, which provides a solid foundation for evaluating novel, previously unseen methods against methods we selected for our experiment.