# Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2204.09183 · HTML: https://mlsanity.com/paper/2204.09183/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 23 of 23)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Fast is better than free: Revisiting adversarial training](https://mlsanity.com/paper/2001.03994/) (2020)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (2017)
- [A Semantic Loss Function for Deep Learning with Symbolic Knowledge](https://mlsanity.com/paper/1711.11157/) (2017)
- [Towards Deep Neural Network Architectures Robust to Adversarial Examples](https://mlsanity.com/paper/1412.5068/) (2014)
- [A Survey of Privacy Attacks in Machine Learning](https://mlsanity.com/paper/2007.07646/) (2020)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [Towards the Science of Security and Privacy in Machine Learning](https://mlsanity.com/paper/1611.03814/) (2016)
- [Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities](https://mlsanity.com/paper/2003.13213/) (2020)
- [Adversarial Examples in Deep Learning for Multivariate Time Series Regression](https://mlsanity.com/paper/2009.11911/) (2020)
- [ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems](https://mlsanity.com/paper/2003.05631/) (2020)
- [Real-Time Context-aware Detection of Unsafe Events in Robot-Assisted Surgery](https://mlsanity.com/paper/2005.03611/) (2020)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [Cost-Sensitive Robustness against Adversarial Examples](https://mlsanity.com/paper/1810.09225/) (2018)
- [Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)](https://mlsanity.com/paper/2009.10149/) (2020)
- [Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems](https://mlsanity.com/paper/2105.10707/) (2021)
- [Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems](https://mlsanity.com/paper/2104.02545/) (2021)
- [Strategic Safety-Critical Attacks Against an Advanced Driver Assistance System](https://mlsanity.com/paper/2204.06768/) (2022)
- [Universal Adversarial Attack on Deep Learning Based Prognostics](https://mlsanity.com/paper/2109.07142/) (2021)

## Built on by (most cited first, 5 of 5)

- [Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques](https://mlsanity.com/paper/2502.13256/) (2025)
- [Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems](https://mlsanity.com/paper/2401.17136/) (2024)
- [Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention](https://mlsanity.com/paper/2303.09913/) (2023)
- [KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems](https://mlsanity.com/paper/2311.07460/) (2023)
- [Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems](https://mlsanity.com/paper/2504.03494/) (2025)

## Abstract (arXiv)

The growing complexity of Cyber-Physical Systems (CPS) and challenges in ensuring safety and security have led to the increasing use of deep learning methods for accurate and scalable anomaly detection. However, machine learning (ML) models often suffer from low performance in predicting unexpected data and are vulnerable to accidental or malicious perturbations. Although robustness testing of deep learning models has been extensively explored in applications such as image classification and speech recognition, less attention has been paid to ML-driven safety monitoring in CPS. This paper presents the preliminary results on evaluating the robustness of ML-based anomaly detection methods in safety-critical CPS against two types of accidental and malicious input perturbations, generated using a Gaussian-based noise model and the Fast Gradient Sign Method (FGSM). We test the hypothesis of whether integrating the domain knowledge (e.g., on unsafe system behavior) with the ML models can improve the robustness of anomaly detection without sacrificing accuracy and transparency. Experimental results with two case studies of Artificial Pancreas Systems (APS) for diabetes management show that ML-based safety monitors trained with domain knowledge can reduce on average up to 54.2% of robustness error and keep the average F1 scores high while improving transparency.