# Emerging Paradigms and Architectures for Industry 4.0 Applications
###### Abstract
In the proposed system, the user's sign key is encrypted with a secret key that can be computed with his/her password, while a secret parameter is stored in a secondary device to protect the key. The feasibility of such a system is shown through a prototype that made use of standard cryptographic algorithms (AES-256, RSA-3072, and ECDSA-256), whose performance and security are evaluated. In [7], the authors propose a novel methodology to learn Industry 4.0 and IIoT cybersecurity through practical use cases carried out with the help of free online tools such as Shodan. The described approach was tested during the COVID-19 pandemic lockdowns, showing that students were able to find that 13% of the analyzed IIoT/Industry 4.0 systems could be accessed really easily. Thus, the article provides useful guidelines for teaching industrial cybersecurity and thus trains the next generation of security researchers and developers. Other relevant Industry 4.0 technologies were studied in [8; 9; 10]. In [8], the authors analyze the problem of how to monitor and control additive manufacturing processes in real-time. With such a purpose, the paper identifies real-time machine learning algorithms to analyze the received data and then execute control functions, and then proposes a new architecture, which is illustrated through a practical industrial example. Regarding the work detailed in [9], it analyzes in detail the operation principles of a wind harvester to obtain its characteristic parameters and to create an equivalent electromechanical model. The accuracy of such a model is verified through a prototype, whose AC/DC converter architecture was optimized in terms of parameters such as efficiency, voltage levels, operation frequency, duty cycle, or load. With respect to [10], a perceptual system is proposed to simulate the ventral flow of the human perception system. The main objective of the proposed solution is to simulate human senses that can be later used to estimate user comfort. For such a purpose, a Spatio-temporal Convolutional Neural Network (S-CNN) and a concatenated HoppingNet temporal CNN (T-CNN) are used. The shown results indicate that the proposed system is highly accurate and robust. Finally, in [11], the authors provide an integrated reference model for digital manufacturing platforms, which is based on cutting-edge reference models for IIoT. Thus, the article analyzes the most relevant reference models for IIoT systems to align their definitions and to determine to what extent they are complementary. As a result, the Industrial Internet Integrated Reference Model (I3 RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform. All authors contributed equally to the article. All authors have read and agreed to the published version of the manuscript. This work has been supported through funds ED431G 2019/01 provided by Centro de Investigacion de Galicia \"CITIC\" for a research stay in Instituto Tecnico de Viana do Castelo. This work has also been funded by the Xunta de Galicia (by grant ED431C 2020/15), the Agencia Estatal de Investigacion of Spain (by grants PID2020-118857 RA-I00 and PID2019-104958 RB-C42) and ERDF funds of the EU (FEDER Galicia 2014-2020 & AEI/FEDER Programs, UE). The authors declare no conflict of interest.