# Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components

arXiv: https://arxiv.org/abs/2001.05014 · HTML: https://mlsanity.com/paper/2001.05014/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [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)
- [Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning](https://mlsanity.com/paper/2110.03120/) (2021)
- [Improving Prediction Confidence in Learning-Enabled Autonomous Systems](https://mlsanity.com/paper/2110.03123/) (2021)

## Abstract (arXiv)

Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, they may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we investigate how to use the conformal prediction framework for assurance monitoring of CPS with machine learning components. In order to handle high-dimensional inputs in real-time, we compute nonconformity scores using embedding representations of the learned models. By leveraging conformal prediction, the approach provides well-calibrated confidence and can allow monitoring that ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. Empirical evaluation results using the German Traffic Sign Recognition Benchmark and a robot navigation dataset demonstrate that the error rates are well-calibrated while the number of alarms is small. The method is computationally efficient, and therefore, the approach is promising for assurance monitoring of CPS.