scholarly journals Discussion on IoT Security Recommendations against the State-of-the-Art Solutions

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1814
Author(s):  
Marta Chmiel ◽  
Mateusz Korona ◽  
Fryderyk Kozioł ◽  
Krzysztof Szczypiorski ◽  
Mariusz Rawski
Keyword(s):  
State Of The Art ◽  
Cyber Physical Systems ◽  
Process Data ◽  
Iot Security ◽  
Physical Systems ◽  
Government Organizations ◽  
Security Technologies ◽  
Advisory Groups ◽  
Comprehensive Survey ◽  
The Internet Of Things

The Internet of Things (IoT) is an emerging concept comprising a wide ecosystem of interconnected devices and services. These technologies collect, exchange and process data in order to dynamically adapt to a specific context. IoT is tightly bound to cyber-physical systems and, in this respect, has relevant security implications. A need for IoT security guidelines was identified by the industry in the early 2010s. While numerous institutions across the globe have proposed recommendations with a goal to help developers, distributors and users to ensure a secure IoT infrastructure, a strict set of regulations for IoT security is yet to be established. In this paper, we aim to provide an overview of security guidelines for IoT proposed by various organizations, and evaluate some of the existing technologies applied to ensure IoT security against these guidelines. We gathered recommendations proposed by selected government organizations, international associations and advisory groups, and compiled them into a set of the most common and important considerations, divided into eight categories. Then we chose a number of representative examples from IoT security technologies and evaluated them against these criteria. While none of the examined solutions fulfill all recommendations on their own, the existing technologies introduced by those solutions could be combined to create a design framework which satisfies all the requirements of a secure IoT device. Further research on this matter could be beneficial. To the best of our knowledge, this is the first comprehensive survey to evaluate different security technologies for IoT device security against the compilation of criteria based on existing guidelines.

scholarly journals Artificial Intelligence and the Internet of Things in Industry 4.0

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Omar Santos
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Leading Industry ◽  
Key Technologies ◽  
The Internet Of Things ◽  
State Of Art ◽  
Social Level

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.

scholarly journals How connectivity and search for producers impact production in Industry 4.0 networks

2018 ◽  
Vol 15 (4) ◽  
pp. 528-534
Author(s):  
Adriano Pereira ◽  
Eugênio De Oliveira Simonetto ◽  
Goran Putnik ◽  
Helio Cristiano Gomes Alves de Castro
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Cyber Physical Systems ◽  
Average Degree ◽  
Collaborative Networks ◽  
Production Network ◽  
Simulation Environment ◽  
Physical Systems ◽  
Study Results ◽  
The Internet Of Things

Technological evolutions lead to changes in production processes; the Fourth Industrial Revolution has been called Industry 4.0, as it integrates Cyber-Physical Systems and the Internet of Things into supply chains. Large complex networks are the core structure of Industry 4.0: any node in a network can demand a task, which can be answered by one node or a set of them, collaboratively, when they are connected. In this paper, the aim is to verify how (i) network's connectivity (average degree) and (ii) the number of levels covered in nodes search impacts the total of production tasks completely performed in the network. To achieve the goal of this paper, two hypotheses were formulated and tested in a computer simulation environment developed based on a modeling and simulation study. Results showed that the higher the network's average degree is (their nodes are more connected), the greater are the number of tasks performed; in addition, generally, the greater are the levels defined in the search for nodes, the more tasks are completely executed. This paper's main limitations are related to the simulation process, which led to a simplification of production process. The results found can be applied in several Industry 4.0 networks, such as additive manufacturing and collaborative networks, and this paper is original due to the use of simulation to test this kind of hypotheses in an Industry 4.0 production network.

scholarly journals ARCH-COMP 2019 Category Report: Falsification

10.29007/68dk ◽  
2019 ◽  
Author(s):  
Gidon Ernst ◽  
Paolo Arcaini ◽  
Alexandre Donzé ◽  
Georgios Fainekos ◽  
Logan Mathesen ◽  
...  
Keyword(s):  
Temporal Logic ◽  
State Of The Art ◽  
Cyber Physical Systems ◽  
The State ◽  
Base Line ◽  
Physical Systems ◽  
The Future ◽  
Friendly Competition ◽  
Initial Base

This report presents the results from the 2019 friendly competition in the ARCH workshop for the falsification of temporal logic specifications over Cyber-Physical Systems. We describe the organization of the competition and how it differs from previous years. We give background on the participating teams and tools and discuss the selected benchmarks and results. The benchmarks are available on the ARCH website1, as well as in the competition’s gitlab repository2. The main outcome of the 2019 competition is a common benchmark repository, and an initial base-line for falsification, with results from multiple tools, which will facilitate comparisons and tracking of the state-of-the-art in falsification in the future.

Detecting Intrusions in Cyber-Physical Systems of Smart Cities

2019 ◽  
pp. 74-102 ◽  
Author(s):  
Ismail Butun ◽  
Patrik Österberg
Keyword(s):  
Anomaly Detection ◽  
Smart Cities ◽  
Cyber Physical Systems ◽  
Security And Privacy ◽  
Security Vulnerabilities ◽  
Physical Systems ◽  
Detection Systems ◽  
Comprehensive Survey ◽  
Privacy Violation ◽  
Detection Schemes

Interfacing the smart cities with cyber-physical systems (CPSs) improves cyber infrastructures while introducing security vulnerabilities that may lead to severe problems such as system failure, privacy violation, and/or issues related to data integrity if security and privacy are not addressed properly. In order for the CPSs of smart cities to be designed with proactive intelligence against such vulnerabilities, anomaly detection approaches need to be employed. This chapter will provide a brief overview of the security vulnerabilities in CPSs of smart cities. Following a thorough discussion on the applicability of conventional anomaly detection schemes in CPSs of smart cities, possible adoption of distributed anomaly detection systems by CPSs of smart cities will be discussed along with a comprehensive survey of the state of the art. The chapter will discuss challenges in tailoring appropriate anomaly detection schemes for CPSs of smart cities and provide insights into future directions for the researchers working in this field.

A comprehensive survey on modeling of cyber‐physical systems

2018 ◽  
Vol 32 (15) ◽  
Author(s):  
Imen Graja ◽  
Slim Kallel ◽  
Nawal Guermouche ◽  
Saoussen Cheikhrouhou ◽  
Ahmed Hadj Kacem
Keyword(s):  
Cyber Physical Systems ◽  
Physical Systems ◽  
Comprehensive Survey

Internet of Things and Cyber-Physical Systems at the University

2020 ◽  
pp. 285-302
Author(s):  
Dmitry Namiot ◽  
Manfred Sneps-Sneppe
Keyword(s):  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Smart Cities ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Physical Systems ◽  
Information And Communication ◽  
The University ◽  
Educational Course ◽  
The Internet Of Things

This chapter describes proposals for organizing university programs on the internet of things (IoT) and cyber-physical systems. The final goal is to provide a structure for a basic educational course for the internet of things and related areas. This base (template) could be used both for direct training and for building other courses, including those that are more deeply specialized in selected areas. For related areas, the authors see, for example, machine-to-machine communications and data-driven cities (smart cities) development. Obviously, the internet of things skills are in high demand nowadays, and, of course, IoT models, architectures, as well as appropriate data proceedings elements should be presented in the university courses. The purpose of the described educational course is to cover information and communication technologies used in the internet of things systems and related areas. Also, the authors discuss big data and AI issues for IoT courses and highlight the importance of data engineering.

scholarly journals Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 59
Author(s):  
Fabrizio De Vita ◽  
Dario Bruneo
Keyword(s):  
Intelligent Systems ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Training Data ◽  
Cloud Platform ◽  
Model Accuracy ◽  
Training Time ◽  
Physical Systems ◽  
The Internet Of Things ◽  
And Training

During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy.

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