Typically, Cyber-Physical Systems (CPS) involve various interconnected systems, which can monitor and manipulate real objects and processes. They are closely related to Internet of Things (IoT) systems, except that CPS focuses on the interaction between physical, networking and computation processes. Their integration with IoT led to a new CPS aspect, the Internet of Cyber-Physical Things (IoCPT). The fast and significant evolution of CPS affects various aspects in people's way of life and enables a wider range of services and applications including e-Health, smart homes, e-Commerce, etc. However, interconnecting the cyber and physical worlds gives rise to new dangerous security challenges. Consequently, CPS security has attracted the attention of both researchers and industries. This paper surveys the main aspects of CPS and the corresponding applications, technologies, and standards. Moreover, CPS security vulnerabilities, threats and attacks are reviewed, while the key issues and challenges are identified. Additionally, the existing security measures are presented and analyzed while identifying their main limitations. Finally, several suggestions and recommendations are proposed benefiting from the lessons learned throughout this comprehensive review.
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http://dx.doi.org/10.1016/j.micpro.2020.103201 | 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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December 2024
HPC Laboratory, Department of Engineering and Geology, University "G. d'Annunzio" Chieti-Pescara, Pescara, Italy.
The construction industry is rapidly adopting Industry 4.0 technologies, creating new opportunities to address persistent environmental and operational challenges. This review focuses on how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are being leveraged to tackle these issues.
View Article and Find Full Text PDFVis Comput Ind Biomed Art
December 2024
Department of Electrical Engineering and Computer Sciences, Center for Cyber-Physical Systems, Khalifa University of Science and Technology, Abu Dhabi, 127788, United Arab Emirates.
With the exponential rise in global air traffic, ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security. Although X-ray baggage monitoring is now standard, manual screening has several limitations, including the propensity for errors, and raises concerns about passenger privacy. To address these drawbacks, researchers have leveraged recent advances in deep learning to design threat-segmentation frameworks.
View Article and Find Full Text PDFEur Respir Rev
October 2024
Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, Paris, France
Digital twins have recently emerged in healthcare. They combine advances in cyber-physical systems, modelling and computation techniques, and enable a bidirectional flow of information between the physical and virtual entities. In respiratory medicine, progress in connected devices and artificial intelligence make it technically possible to obtain digital twins that allow real-time visualisation of a patient's respiratory health.
View Article and Find Full Text PDFMethods Appl Fluoresc
December 2024
Physics, Indian Institute of Technology Delhi, Room No.: WS-417,, Department of Physics, IIT Delhi, Hauz Khas New Delhi, Delhi, Delhi, 110016, INDIA.
The current culture-based bacterial detection technique is time-consuming and requires an extended sample preparation methodology. We propose the potential of surface-enhanced Raman spectroscopy (SERS) and surface plasmon-enhanced auto-fluorescence spectroscopy (SPEAS) for the label-free identification and quantification of bacterial pathogens at low concentrations collecting its unique auto-fluorescence and Raman signatures utilising highly anisotropic three-dimensional nanostructures of silver nano dendrites (Ag-NDs). The SERS data facilitates qualitative bacterial identification using the spectral features coming from the bacterial cell wall compound, and the SPEAS data was utilised to gain unique auto-fluorescence spectra present on the bacterial cell wall with enhanced quantification.
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