11 research outputs found
Control of teleoperation systems in the presence of cyber attacks: A survey
No full textThe teleoperation system is often composed of a human operator, a local master manipulator, and a remote slave manipulator that are connected by a communication network. This paper proposes a survey on feedback control design for the bilateral teleoperation systems (BTSs) in nominal situations and in the presence of cyber-attacks. The main idea of the presented methods is to achieve the stability of a delayed bilateral teleoperation system in the presence of several kinds of cyber attacks. In this paper, a comprehensive survey on control systems for BTSs under cyber-attacks is discussed. Finally, we discuss the current and future problems in this field. The teleoperation system is often composed of a human operator, a local master manipulator, and a remote slave manipulator that are connected by a communication network. This paper proposes a survey on feedback control design for the bilateral teleoperation systems (BTSs) in nominal situations and in the presence of cyber-attacks. The main idea of the presented methods is to achieve the stability of a delayed bilateral teleoperation system in the presence of several kinds of cyber attacks. In this paper, a comprehensive survey on control systems for BTSs under cyber-attacks is discussed. Finally, we discuss the current and future problems in this field
Analysis and Challenges in Wireless Networked Control System: A Survey
No full textA wireless networked control system (WNCS) consists of a dynamic system to be controlled, sensors, actuators, and a remote controller. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In this paper, we are surveying the literature on the communication networks in WNCSs and the challenges related to them, such as the communication standards, delay, Packet dropout, and delay jitter. Then, the control approaches in the design of a WNCS are presented, including the interactive design approaches and the joint design approaches. Also, several applications of WNCSs have been discussed in terms of their structure, functionality, and control design. These applications include Intra-Vehicle Wireless networks, Wireless Avionics Intra-Communication, Building Automation, and Water pumping. After that, security issues in WNCSs from a control engineering point of view are detailed while focusing on the major kinds of cyber attacks affecting WNCSs. Finally, future directions and conclusions are summarized at the end of the paper
Cyberphysical infrastructures in power systems architectures and vulnerabilities
No full textIn an uncertain and complex environment, to ensure secure and stable operations of large-scale power systems is one of the biggest challenges that power engineers have to address today. Traditionally, power system operations and decision-making in controls are based on power system computations of physical models describing the behavior of power systems. Largely, physical models are constructed according to some assumptions and simplifications, and such is the case with power system models. However, the complexity of power system stability problems, along with the system's inherent uncertainties and nonlinearities, can result in models that are impractical or inaccurate. This calls for adaptive or deep-learning algorithms to significantly improve current control schemes that solve decision and control problems.Cyberphysical Infrastructures in Power Systems: Architectures and Vulnerabilities provides an extensive overview of CPS concepts and infrastructures in power systems with a focus on the current state-of-the-art research in this field. Detailed classifications are pursued highlighting existing solutions, problems, and developments in this area.- Gathers the theoretical preliminaries and fundamental issues related to CPS architectures.- Provides coherent results in adopting control and communication methodologies to critically examine problems in various units within smart power systems and microgrid systems.- Presents advanced analysis under cyberphysical attacks and develops resilient control strategies to guarantee safe operation at various power level
An Adaptive Sliding Mode Control for Single Machine Infinite Bus System under Unknown Uncertainties
No full textThe inherent uncertainties in a Single Machine Infinite Bus System (SMIBS) are governed by unmodeled dynamics or large disturbances such as the system's faults. The existence of these uncertainties demands robust controllers to guarantee the system's asymptotic stability under such exacting conditions. In this work, we propose an Adaptive Sliding Mode Control (ASMC) design implemented on a fifth-order nonlinear SMIBS to handle those uncertainties without prior knowledge about its upper bounds. We develop the ASMC with gains of two nested adaptive layers to asymptotically stabilize the system's internal states, the machine's terminal voltage, and power angle within a region of unknown bounded uncertainties while mitigating the chattering phenomena associated with conventional Sliding Mode Control (SMC). To verify the design's effectiveness and prove the conducted Lyapunov theoretical stability analysis, we simulate the occurrence of a large disturbance represented by a 3-phase fault at the system's universal bus. The results show that the ASMC can successfully achieve asymptotic stable output errors and stabilizing the SMIBS internal states after the clearance of the fault. Moreover, the ASMC noticeably outperforms the SMC in chattering mitigation, where the ASMC's signal is significantly smoother than that of the SMC
Wide area monitoring system operations in modern power grids: A median regression function-based state estimation approach towards cyber attacks
No full textModern power grid is a generation mix of conventional generation facilities and variable renewable energy resources (VRES). The complexity of such a power grid with generation mix has routed the utilization of infrastructures involving phasor measurement units (PMUs). This is to have access to real-time grid information. However, the traffic of digital information and communication is potentially vulnerable to data-injection and cyber attacks. To address this issue, a median regression function (MRF)-based state estimation is presented in this paper. The algorithm was stationed at each monitoring node using interacting multiple model (IMM)-based fusion architecture. An exogenous variable-driven representation of the state is considered for the system. A mapping function-based initial regression analysis is made to depict the margins of state estimate in the presence of data-injection. A median regression function is built on top of it while generating and evaluating the residuals. The tests were conducted on a revisited New England 39-Bus system with large scale photovoltaic (PV) power plant. The system was affected with multiple system disturbances and severe data-injection attacks. The results show the effectiveness of the proposed MRF method against the mainstream and regression methods. The proposed scheme can accurately estimate the states and evaluate the contaminated measurements while improving the situation awareness of wide area monitoring systems (WAMS) operations in modern power grids 2023 The Author(s)The publication of this article was funded by Qatar National Library .Scopu
Stability Analysis of Cyber-physical System Under Transmission Delay
No full textWith the intimate integration of power grids and cyber networks, limited bandwidth and packet delay have a rapidly expanding negative impact on power system performance. The presented multi-area interconnected power system consists of four areas, each including thermal and hydro-generation plants. This paper investigates the stability analysis problem for cyber-physical systems with a round-robin communication protocol under mixed cyberattacks and load changes. The objective is to stabilize a multi-area interconnected power system (MAIPS) using a static feedback controller while minimizing the defined performance function. Then, the stability of the MAIPS is characterized when the system is subjected to a transmission delay while considering predetermined limits for the duration and the frequency of the delay. Our findings indicate that time delays can influence system stability and that choosing an appropriate sampling interval is necessary to ensure the stability of the system. Finally, an illustrative example of three areas of interconnected power systems with several scenarios is presented to verify the effectiveness of the proposed method
Deep Learning-based Attack Detector for Bilateral Teleoperation Systems
No full textA teleoperation system is referred to as a plant that is controlled remotely, and it is often composed of a human operator, a local master manipulator, and a remote slave manipulator, all connected by a communication network. Bilateral teleoperation systems (BTOS) include transmissions in both the forward and backward directions between the master and slave. This paper discusses a class of (BTOS) focusing on the security of the system after modeling the master and slave robots mathematically. The false data injection attack is examined, where the attacker may inject false data into the states that are being exchanged between the master and slave robots. The vulnerability of BTOS, where the attack destabilizes the system, is presented. A deep learning-based detection technique is proposed to detect the presence of false data injection attacks. The deep learning model with convolution neural network structure is trained and tested with considering complex attacks where the attacker has full knowledge of the system and proficiency to emanate and control the target system. The proposed model achieves 96\% validation accuracy, and the efficacy of the proposed deep learning detector is demonstrated and tested into the BTOS
A Machine Learning Based Vehicle Classification in Forward Scattering Radar
No full textThe Forward scattering radars (FSRs) are special types of Bistatic radars in which detected targets should exist in the narrow baseline to obtain their tracking at an angle of 180 degree. This gives the radar several features such as target classification which makes FSR more privileged in comparison to traditional radar systems. Existing research works concerning the ground target detection and classification have utilized neural network for the identification processes and compared it to other statistical models in terms of signal complexity. However, these works considered limited number of scenarios and thereby, the results are insufficient to create an automatic classification system. This study investigates and analyses the classification of ground targets in FSR using Machine-learning (ML) techniques, and proposes a hybrid model for ground target classification. The analysis in this paper represent a foundation for a potential use of pre-processing and signal processing techniques, statistical analysis, and ML in radar applications. The obtained results show that the k-nearest neighbor classifier (KNN) achieves the best performance in all examined scenarios. Additionally, combining multiple pre-processing techniques enhances the accuracy of classification by approximately 30.2% and increases the overall accuracy to more than 99%
Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network
No full textWireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate
