Biblio

The (IoT) paradigm’s fundamental goal is to massively connect the “smart things” through standardized interfaces, providing a variety of smart services. Cyber-Physical Systems (CPS) include both physical and cyber components and can apply to various application domains (smart grid, smart transportation, smart manufacturing, etc.). The Digital Twin (DT) is a cyber clone of physical objects (things), which will be an essential component in CPS. This paper designs a systematic taxonomy to explore different attacks on DT-based CPS and how they affect the system from a four-layer architecture perspective. We present an attack space for DT-based CPS on four layers (i.e., object layer, communication layer, DT layer, and application layer), three attack objects (i.e., confidentiality, integrity, and availability), and attack types combined with strength and knowledge. Furthermore, some selected case studies are conducted to examine attacks on representative DT-based CPS (smart grid, smart transportation, and smart manufacturing). Finally, we propose a defense mechanism called Secured DT Development Life Cycle (SDTDLC) and point out the importance of leveraging other enabling techniques (intrusion detection, blockchain, modeling, simulation, and emulation) to secure DT-based CPS.

With the increasing of health awareness, the users become more and more interested in their daily health information and healthcare activities results from healthcare organizations. They always try to collect them together for better usage. Traditionally, the healthcare data is always delivered by paper format from the healthcare organizations, and it is not easy and convenient for data usage and management. They would have to translate these data on paper to digital version which would probably introduce mistakes into the data. It would be necessary if there is a secure and convenient method for electronic health data transferring between the users and the healthcare organizations. However, for the security and privacy problems, almost no healthcare organization provides a stable and full service for health data delivery. In this paper, we propose a secure and convenient method, QRStream, which splits original health data and loads them onto QR code frame streaming for the data transferring. The results shows that QRStream can transfer text health data smoothly with an acceptable performance, for example, transferring 10K data in 10 seconds.