Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities

TL;DR

This paper demonstrates that condition-based maintenance systems relying on machine learning are vulnerable to adversarial attacks, highlighting the need for robust defense strategies against such threats.

Contribution

It introduces the application of adversarial machine learning techniques to CBM systems and provides a case study showing their effectiveness in compromising these systems.

Findings

CBM systems are susceptible to adversarial attacks using FGSM.

Adversarial perturbations can significantly degrade CBM performance.

Defense strategies are necessary to protect CBM from adversarial threats.

Abstract

Condition-based maintenance (CBM) strategies exploit machine learning models to assess the health status of systems based on the collected data from the physical environment, while machine learning models are vulnerable to adversarial attacks. A malicious adversary can manipulate the collected data to deceive the machine learning model and affect the CBM system's performance. Adversarial machine learning techniques introduced in the computer vision domain can be used to make stealthy attacks on CBM systems by adding perturbation to data to confuse trained models. The stealthy nature causes difficulty and delay in detection of the attacks. In this paper, adversarial machine learning in the domain of CBM is introduced. A case study shows how adversarial machine learning can be used to attack CBM capabilities. Adversarial samples are crafted using the Fast Gradient Sign method, and the performance of a CBM system under attack is investigated. The obtained results reveal that CBM systems are vulnerable to adversarial machine learning attacks and defense strategies need to be considered.