Titlebook: Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis; Sujit Rokka Chhetri,Mohammad Abdullah Al Faruque Book 2020 Spr

Chauvinistic

Danny de Jesús Gómez-Ramírez,Alan Smaillvel dynamic graph embedding algorithm to handle this issue. In the rest of the chapter, we consider temporally evolving graphs as the non-euclidean data and present an algorithm capable of capturing the pattern of time-varying links.

Cardioversion

Introduction,s, govern the physical dynamics of the system. Due to the juxtaposition of cross-layer components (., etc.) and cross-domain components, CPS provides various technology solutions to multiple fields (., etc.).

平淡而無味

Ingrained

Dynamic Graph Embeddingvel dynamic graph embedding algorithm to handle this issue. In the rest of the chapter, we consider temporally evolving graphs as the non-euclidean data and present an algorithm capable of capturing the pattern of time-varying links.

左右連貫

巫婆

Non-euclidean Data-Driven Modeling Using Graph Convolutional Neural Networkson data-driven modeling algorithms for non-euclidean data. We specifically focus on developing algorithms for handling non-euclidean data present in cyber-physical systems. To this end, in this chapter we present a novel non-euclidean data-driven modeling approach using graph convolutional neural network.

Externalize

四海為家的人

978-3-030-37964-3Springer Nature Switzerland AG 2020

名義上

Rheumatologist

https://doi.org/10.1007/b101764ion in the cyber-domain manifests in terms of physical actions (such as motion, temperature change, etc.). However, this interaction may make the system vulnerable to physical-to-cyber domain attacks. These attacks affect the confidentiality of the system by utilizing the physical actions, which are