At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.
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http://dx.doi.org/10.3390/s23156935 | DOI Listing |
Data Brief
December 2024
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
With the development of smart buildings, the risks of cyber-attacks against them have also increased. One of the popular and evolving protocols used for communication between devices in smart buildings, especially HVAC systems, is the BACnet protocol. Machine learning algorithms and neural networks require datasets of normal traffic and real attacks to develop intrusion detection (IDS) and prevention (IPS) systems that can detect anomalies and prevent attacks.
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October 2024
Universidad Europea del Atlántico, Santander, Spain.
The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology.
View Article and Find Full Text PDFHeliyon
October 2024
Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia.
Automatic Generation Control (AGC) systems in smart grids are increasingly vulnerable to cyber-attacks, particularly False Data Injection (FDI) attacks, due to their reliance on information and communication technologies. These vulnerabilities pose significant threats to the reliable operation of power systems. To address this challenge, this research paper introduces the machine learning (ML) based cyberattack detection technique designed to identify FDI attacks with the highest accuracy proficiently.
View Article and Find Full Text PDFHeliyon
November 2024
Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Distributed generation (DG) systems are becoming more popular due to several benefits such as clean energy, decentralization, and cost effectiveness. Because the majority of renewable energy sources provide DC power, power electronic inverters are necessary for their conversion from DC to AC power. To fulfill this demand, the next generation power inverter employs innovative technologies while simultaneously assuring stability and resilience.
View Article and Find Full Text PDFHeliyon
October 2024
Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur AJK, 10250, Pakistan.
Smart grids arose as the largest cyber-physical systems with the integration of sophisticated control, computing, and state-of-the-art communications. Like all cyber-physical systems, the smart grids are vulnerable to malicious cyber assaults due to their enormous dependency on communication networks. Various machine learning-based schemes are being investigated in the industry and academia to develop robust defense mechanisms to counter cyber assaults.
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