Multi Hardware-Attack Dataset and ML-based Detection Using Processor Stress Patterns on x86
David Andreu, Beatriz Otero, Ramon Canal · WiPiEC Journal - Works in Progress in Embedded Computing Journal · 2025
Hardware attacks exploit the vulnerabilities discovered in state-of-the-art CPUs. As an example, attacks such as Meltdown and Spectre have made the headlines. To benefit from the vulnerabilities, hardware attacks stress tremendously some section/s of the processor, usually the branch-prediction unit and the different cache levels. This gives us a recognizable pattern and a way to implement a system capable of detecting the presence of these attacks while monitoring the computer.In this paper, we describe the set of hardware attacks under focus, then we describe how we create the dataset and, finally, the use of machine learning to detect the attacks in three scenarios (i.e. training on both benign applications and attacks, training on only benign applications and training only on attacks). The techniquesproposed are capable of achieving over 99% detection rate with a machine learning model. This provides a run-time solution to quickly identify the attack as it starts running and take remedial actions.