With the rise and growing attention on Digital Twins (DT) as a way to provide integration between the Internet of Things (IoT) and data analytics, so does the need to consider how to address its challenges. To deal with these challenges, Named Data Networking (NDN) can be a possible solution. NDN has been rising in popularity due to its advancements over the traditional TCP/IP Internet architecture. In this paper, our approach begins with the framework that leverages an NDN-based DT architecture for data management. We then design two scenarios that focus on the performance of data querying in a small and large-scale simulated NDN-based DT architecture. Based on the designed scenarios, we conduct the performance evaluation of data query and DT performance to investigate the performance gap and determine whether an action needs to be taken.

The applications of machine learning (ML) in cyber-physical systems (CPS), such as the smart energy grid has increased significantly. While ML technology can be integrated into CPS, the security risk of ML technology has to be considered. In particular, adversarial examples provide inputs to a ML model with intentionally attached perturbations (noise) that could pose the model to make incorrect decisions. Perturbations are expected to be small or marginal so that adversarial examples could be invisible to humans, but can significantly affect the output of ML models. In this paper, we design a taxonomy to provide the problem space for investigating the adversarial example generation techniques based on state-of-the-art literature. We propose a three-dimensional framework containing three dimensions for adversarial attack scenarios (i.e., black-box, white-box, and gray-box), target type, and adversarial examples generation methods (gradient-based, score-based, decision-based, transfer- based, and others). Based on the designed taxonomy, we systematically review the existing research efforts on adversarial ML in representative CPS (i.e., transportation, healthcare, and energy). Furthermore, we provide one case study to demonstrate the impact of adversarial examples of attacks on a smart energy CPS deployment. The results indicate that the accuracy can decrease significantly from 92.62% to 55.42% with a 30% adversarial sample injection. Finally, we discuss potential countermeasures and future research directions for adversarial ML.