ﻻ يوجد ملخص باللغة العربية
It has been challenging for the technical and regulatory communities to formulate requirements for trustworthiness of the cyber-physical systems (CPS) due to the complexity of the issues associated with their design, deployment, and operations. The US National Institute of Standards and Technology (NIST), through a public working group, has released a CPS Framework that adopts a broad and integrated view of CPS and positions trustworthiness among other aspects of CPS. This paper takes the model created by the CPS Framework and its further developments one step further, by applying ontological approaches and reasoning techniques in order to achieve greater understanding of CPS. The example analyzed in the paper demonstrates the enrichment of the original CPS model obtained through ontology and reasoning and its ability to deliver additional insights to the developers and operators of CPS.
This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS (e.g., robotics), where the low-level control is based on e.g., the extended
Industrial cyber-physical systems (ICPSs) manage critical infrastructures by controlling the processes based on the physics data gathered by edge sensor networks. Recent innovations in ubiquitous computing and communication technologies have prompted
Cyber-Physical Systems (CPSs) are increasingly important in critical areas of our society such as intelligent power grids, next generation mobile devices, and smart buildings. CPS operation has characteristics including considerable heterogeneity, va
The proliferation of digitization and complexity of connectivity in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate the functionality and security of critical infrastructures. In this regard, Digital Twins (DTs) are revolutioniz
Cyber-physical systems, such as self-driving cars or autonomous aircraft, must defend against attacks that target sensor hardware. Analyzing system design can help engineers understand how a compromised sensor could impact the systems behavior; howev