Abstract
Learning-Enabled Components (LECs), such as neural networks, are broadly employed in Cyber-Physical Systems (CPSs) to tackle a wide variety of complex tasks in high-uncertainty environments. However, the training dataset is inevitably incomplete, and Out-Of-Distribution (OOD) data not encountered during the LEC training may lead to erroneous predictions, jeopardizing the safety of the system. In this paper, we first analyze the causes of OOD data and define various types of OOD data in learning-enabled CPSs. We propose an approach to effectively detect OOD data for both classification and regression problems. The proposed approach incorporates the variational autoencoder for classification and regression model to the Inductive Conformal Anomaly Detection (ICAD) framework, enabling the detection algorithm to take into consideration not only the LEC inputs but also the LEC outputs. We evaluate the approach using extensive experiments for both classification and regression tasks, and the experimental results validate the effectiveness of the proposed method for detecting different types of OOD data. Furthermore, the execution time of detection is relatively short; therefore, the proposed approach can be used for real-time detection.