A novel method for detecting disk filtration attacks via the various machine learning algorithms

Abstract

Disk Filtration (DF) Malware can attack air-gapped computers. However, none of the existing technique can detect DF attacks. To address this problem, a method for detecting the DF attacks based on the fourteen Machine Learning (ML) algorithms is proposed in this paper. First, we collect a number of data about Power Spectral Density (PSD) and frequency of the sound wave from the Hard Disk Drive (HDD). Second, the corresponding machine learning models are trained respectively using the collected data. Third, the trained ML models are employed to detect whether a DF attack occurs or not respectively, if given pair of values of PSD and frequency are input. The experimental results show that the max accuracy of detection is greater than or equal to 99.4%.