scholarly journals Securing Real-Time Internet-of-Things

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4356 ◽  
Author(s):  
Chien-Ying Chen ◽  
Monowar Hasan ◽  
Sibin Mohan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Manufacturing Systems ◽  
Cyber Physical Systems ◽  
Cyber Attacks ◽  
Power Grids ◽  
Embedded Devices ◽  
Industrial Control Systems ◽  
Physical Systems ◽  
Recent Developments

Modern embedded and cyber-physical systems are ubiquitous. Many critical cyber-physical systems have real-time requirements (e.g., avionics, automobiles, power grids, manufacturing systems, industrial control systems, etc.). Recent developments and new functionality require real-time embedded devices to be connected to the Internet. This gives rise to the real-time Internet-of-things (RT-IoT) that promises a better user experience through stronger connectivity and efficient use of next-generation embedded devices. However, RT-IoT are also increasingly becoming targets for cyber-attacks, which is exacerbated by this increased connectivity. This paper gives an introduction to RT-IoT systems, an outlook of current approaches and possible research challenges towards secure RT-IoT frameworks.

Real‐time Internet of things and cyber‐physical systems

2019 ◽  
Vol 30 (4) ◽  
pp. e3616
Author(s):  
Kyung‐Joon Park ◽  
Kyungtae Kang ◽  
Qixin Wang ◽  
Dongeun Lee
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Managing Cyber Security Risks of the Cyber-Enabled Ship

2020 ◽  
Vol 8 (10) ◽  
pp. 768
Author(s):  
Georgios Kavallieratos ◽  
Sokratis Katsikas
Keyword(s):  
Cyber Security ◽  
Cyber Physical Systems ◽  
Transformation Process ◽  
Cyber Attacks ◽  
Security Requirements ◽  
Shipping Industry ◽  
Security Risks ◽  
Industrial Control Systems ◽  
Physical Systems ◽  
Cyber Risk

One aspect of the digital transformation process in the shipping industry, a process often referred to as Shipping 4.0, is the increased digitization of on board systems that goes along with increased automation in and autonomy of the vessel. This is happening by integrating Information Technology with Operation Technology systems that results in Cyber Physical Systems on which the safe operations and sailing of contemporary and future vessels depend. Unavoidably, such highly interconnected and interdependent systems increase the exposure of the vessel’s digital infrastructure to cyber attacks and cyber security risks. In this paper, we leverage the STRIDE and DREAD methodologies to qualitatively and quantitatively assess the cyber risk of Cyber Physical Systems on board digitalized contemporary and future ships. Further, we propose appropriate cyber security baseline controls to mitigate such risks, by applying a systematic approach using a set of criteria that take into account the security requirements; the cyber risks; the possible attacks; and the possibly already existing controls, to select from the list of controls provided in the Industrial Control Systems (ICS) overlay of the NIST Guide to ICS Security. The results are expected to support the decision-making and the design of a security architecture for the cyber-enabled ship.

scholarly journals Real-Time Sensor Networks and Systems for the Industrial IoT: What Next?

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5023
Author(s):  
Christos Koulamas ◽  
Mihai T. Lazarescu
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Real Time ◽  
Industrial Revolution ◽  
Cyber Physical Systems ◽  
Industrial Internet Of Things ◽  
Physical Systems ◽  
Industrial Internet ◽  
Industrial Iot

The Industrial Internet of Things (Industrial IoT—IIoT) is the emerging core backbone construct for the various cyber-physical systems constituting one of the principal dimensions of the 4th Industrial Revolution [...]

scholarly journals The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: a literature review

2021 ◽  
Vol 113 (7-8) ◽  
pp. 2395-2412
Author(s):  
Baudouin Dafflon ◽  
Nejib Moalla ◽  
Yacine Ouzrout
Keyword(s):  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Review Literature ◽  
Comprehensive Understanding ◽  
Physical Systems ◽  
Fine Grained ◽  
Key Technologies ◽  
Granularity Level ◽  
Human Component

AbstractThis work aims to review literature related to the latest cyber-physical systems (CPS) for manufacturing in the revolutionary Industry 4.0 for a comprehensive understanding of the challenges, approaches, and used techniques in this domain. Different published studies on CPS for manufacturing in Industry 4.0 paradigms through 2010 to 2019 were searched and summarized. We, then, analyzed the studies at a different granularity level inspecting the title, abstract, and full text to include in the prospective study list. Out of 626 primarily extracted relevant articles, we scrutinized 78 articles as the prospective studies on CPS for manufacturing in Industry 4.0. First, we analyzed the articles’ context to identify the major components along with their associated fine-grained constituents of Industry 4.0. Then, we reviewed different studies through a number of synthesized matrices to narrate the challenges, approaches, and used techniques as the key-enablers of the CPS for manufacturing in Industry 4.0. Although the key technologies of Industry 4.0 are the CPS, Internet of Things (IoT), and Internet of Services (IoS), the human component (HC), cyber component (CC), physical component (PC), and their HC-CC, CC-PC, and HC-PC interfaces need to be standardized to achieve the success of Industry 4.0.

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

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