Applying of digital signal processing techniques to improve the performance of machine learning-based cyber attack detection in industrial control system

Abstract

Traditional approaches to building attack detection systems, such as a signature-based approach, do not allow detecting zero-day attacks. To improve the performance of attack detection, one resort to use methods of machine learning, in particular, neural networks. This paper considers the problem of detecting cyber attacks in Industrial Control Systems (ICS) using digital signal processing (DSP) technology. By processing signals from sensors using a comb of digital low-pass filters (LPF), additional informative features, that describe the control system, have been created. Experimental studies were conducted on the Secure Water Treatment (SWaT) dataset, which showed that the use of additional features obtained using DSP technologies improves the accuracy of detecting cyberattacks on ACS by reducing errors of the second kind.