# Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities

arXiv: https://arxiv.org/abs/2101.12097 · HTML: https://mlsanity.com/paper/2101.12097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Technical Report on the CleverHans v2.1.0 Adversarial Examples Library](https://mlsanity.com/paper/1610.00768/) (2016)
- [Adversarial Machine Learning -- Industry Perspectives](https://mlsanity.com/paper/2002.05646/) (2020)
- [Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems](https://mlsanity.com/paper/2004.05005/) (2020)

## Built on by (most cited first, 2 of 2)

- [Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research](https://mlsanity.com/paper/2206.14153/) (2022)
- [Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems](https://mlsanity.com/paper/2505.03120/) (2025)

## Abstract (arXiv)

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.