This article concerns the problem of observer-based event-triggered control for cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. In contrast to the existing studies where DoS attacks on different channels are the same, the considered attacks compromise each channel independently. Correspondingly, a decentralized event-triggered scheme is adopted based on the tradeoff between the transmission efficiency and tolerable attack intensity with guarantees on the closed-loop stability. Inspired by the Lyapunov theory for switched systems, the proposed stabilization criteria reveals a link between the tolerable attack intensity and the event-triggering parameters. An example is finally provided to illustrate the effectiveness of the proposed approaches.
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http://dx.doi.org/10.1109/TCYB.2019.2944956 | DOI Listing |
Sensors (Basel)
January 2025
Department of Automation, "Dunarea de Jos" University of Galati, 800008 Galati, Romania.
This paper deals with a "digital twin" (DT) approach for processing, reprocessing, and scrapping (P/R/S) technology running on a modular production system (MPS) assisted by a mobile cyber-physical robotic system (MCPRS). The main hardware architecture consists of four line-shaped workstations (WSs), a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM) and a mobile visual servoing system (MVSS) mounted on the end effector. The system architecture integrates a hierarchical control system where each of the four WSs, in the MPS, is controlled by a Programable Logic Controller (PLC), all connected via Profibus DP to a central PLC.
View Article and Find Full Text PDFData Brief
February 2025
School of Engineering and Technology, University of New South Wales, Canberra, Australia.
This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF).
View Article and Find Full Text PDFData 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.
View Article and Find Full Text PDFPeerJ Comput Sci
October 2024
Computer Science and Informatics Department, University of La Frontera, Temuco, La Araucanía, Chile.
Autonomous underwater vehicles (AUV) constitute a specific type of cyber-physical system that utilize electronic, mechanical, and software components. A component-based approach can address the development complexities of these systems through composable and reusable components and their integration, simplifying the development process and contributing to a more systematic, disciplined, and measurable engineering approach. In this article, we propose an architecture to design and describe the optimal performance of components for an AUV engineering process.
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.
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