A Research Agenda for Smarter Cyber-Physical Systems

Author(s):  
Danny Weyns ◽  
Jesper Andersson ◽  
Mauro Caporuscio ◽  
Francesco Flammini ◽  
Andreas Kerren ◽  
...  

With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.

Author(s):  
Angelika Musil ◽  
Juergen Musil ◽  
Danny Weyns ◽  
Tomas Bures ◽  
Henry Muccini ◽  
...  

Author(s):  
Vladimir Hahanov ◽  
Volodymyr Miz ◽  
Eugenia Litvinova ◽  
Alexander Mishchenko ◽  
Dmitry Shcherbin

2017 ◽  
Vol 148 ◽  
pp. 257-279 ◽  
Author(s):  
Mischa Schmidt ◽  
M. Victoria Moreno ◽  
Anett Schülke ◽  
Karel Macek ◽  
Karel Mařík ◽  
...  

Author(s):  
Qinxue Li ◽  
Bugong Xu ◽  
Shanbin Li ◽  
Yonggui Liu ◽  
Xuhuan Xie

Owing to the deep integration of the information and communication technologies, power cyber-physical systems (CPSs) have become smart but are vulnerable to cyber attacks. To correctly assess the vulnerability of power CPSs and further study feasible countermeasures, we verify that a data-driven target attack on a nonlinear Granger causality graph (NGCG) can be constructed successfully, even if adversaries cannot acquire the configuration information of the systems. A NGCG is a unified framework for the processing and analysis of nonlinear measurement data or datasets and can be used to evaluate the significance of power nodes or lines. In addition, an algorithm including data-driven parameter estimation, noise removal and data reconstruction based on symplectic geometry is introduced to make the NGCG a parameter-free and noise-tolerant method. In particular, three new indexes on the weight analysis of the NGCG are defined to quantitatively evaluate the significance of power nodes or lines. Finally, several case studies of a nonlinear simulation model and power systems in detail verify the effectiveness and superiority of the proposed data-driven target attack. The results show the proposed target attack can select the key attack targets more accurately and lead to physical system collapse with the least number of attack steps.


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