Paper 2024/1939

Machine Learning-Based Detection of Glitch Attacks in Clock Signal Data

Abstract

Voltage fault injection attacks are a particularly powerful threat to secure embedded devices because they exploit brief, hard-to-detect power fluctuations causing errors or bypassing security mechanisms. To counter these attacks, various detectors are employed, but as defenses strengthen, increasingly elusive glitches continue to emerge. Artificial intelligence, with its inherent ability to learn and adapt to complex patterns, presents a promising solution. This research presents an AI-driven voltage fault injection detector that analyzes clock signals directly. We provide a detailed fault characterization of the STM32F410 microcontroller, emphasizing the impact of faults on the clock signal. Our findings reveal how power supply glitches directly impact the clock, correlating closely with the amount of power injected. This led to developing a lightweight Multi-Layer Perceptron model that analyzes clock traces to distinguish between safe executions, glitches that keep the device running but may introduce faults, and glitches that cause the target to reset. While previous fault injection AI applications have primarily focused on parameter optimization and simulation assistance, in this work we use the adaptability of machine learning to create a fault detection model that is specifically adjusted to the hardware that implements it. The developed glitch detector has a high accuracy showing this a promising direction to combat FI attacks on a variety of platform.