Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

2020 ◽  
Vol 16 (4) ◽  
pp. 2716-2725 ◽  
Author(s):  
Faezeh Farivar ◽  
Mohammad Sayad Haghighi ◽  
Alireza Jolfaei ◽  
Mamoun Alazab
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Omar Santos

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.


Author(s):  
Chengwei Wu ◽  
Weiran Yao ◽  
Wei Pan ◽  
Guanghui Sun ◽  
Jianxing Liu ◽  
...  

Author(s):  
Evren Daglarli

Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.


2021 ◽  
Vol 117 ◽  
pp. 291-298
Author(s):  
Zhihan Lv ◽  
Dongliang Chen ◽  
Ranran Lou ◽  
Ammar Alazab

Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Max Van Kleek ◽  
Omar Santos ◽  
Uchenna Ani

Sign in / Sign up

Export Citation Format

Share Document