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Unknown input attack uncertainty patterns (a) Unknown bounded scaling attack input pattern in per unit basis, (b) Unknown bounded ramp type attack input pattern in per unit basis, (c) Unknown bounded pulse attack uncertainty pattern, (d) Unknown bounded random attack uncertainty pattern in per unit basis
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In this study, an observer based control strategy is proposed for load frequency control (LFC) scheme against cyber-attack uncertainties. Most of research work focused on detection scheme or delay estimation scheme in presence of cyber-attack vulnerabilities and paid less attention on design of counteractive robust control scheme for LFC problem. T...
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Citations
... Sensors 2024, 24, 3461 2 of 28 gain access to the bus [7], they can manipulate communication, including injecting anomalous frames that transmit various erroneous commands or data to targeted vehicle components. The prevalent types of abnormal messages include Denial of Service (DoS) [8], Fuzzing [9,10], Replay [11], Spoofing [12], Scaling attack [13,14], and Ramp attack [13,14]. Researchers have explored the security issues of in-vehicle networks [15]. ...
... Sensors 2024, 24, 3461 2 of 28 gain access to the bus [7], they can manipulate communication, including injecting anomalous frames that transmit various erroneous commands or data to targeted vehicle components. The prevalent types of abnormal messages include Denial of Service (DoS) [8], Fuzzing [9,10], Replay [11], Spoofing [12], Scaling attack [13,14], and Ramp attack [13,14]. Researchers have explored the security issues of in-vehicle networks [15]. ...
... This could potentially compromise the safety and efficiency of the system. The essence of a Scaling attack lies in its simplicity and the potential chaos it can introduce, necessitating the implementation of robust detection and recovery mechanisms to uphold the stability and security of the system [13,14]. ...
As an enhanced version of standard CAN, the Controller Area Network with Flexible Data (CAN-FD) rate is vulnerable to attacks due to its lack of information security measures. However, although anomaly detection is an effective method to prevent attacks, the accuracy of detection needs further improvement. In this paper, we propose a novel intrusion detection model for the CAN-FD bus, comprising two sub-models: Anomaly Data Detection Model (ADDM) for spotting anomalies and Anomaly Classification Detection Model (ACDM) for identifying and classifying anomaly types. ADDM employs Long Short-Term Memory (LSTM) layers to capture the long-range dependencies and temporal patterns within CAN-FD frame data, thus identifying frames that deviate from established norms. ACDM is enhanced with the attention mechanism that weights LSTM outputs, further improving the identification of sequence-based relationships and facilitating multi-attack classification. The method is evaluated on two datasets: a real-vehicle dataset including frames designed by us based on known attack patterns, and the CAN-FD Intrusion Dataset, developed by the Hacking and Countermeasure Research Lab. Our method offers broader applicability and more refined classification in anomaly detection. Compared with existing advanced LSTM-based and CNN-LSTM-based methods, our method exhibits superior performance in detection, achieving an improvement in accuracy of 1.44% and 1.01%, respectively.
... A recent review study on LFC illustrated that malicious cyberattacks pose severe issues using CPPSs [11]. Due to CPPS structure, the LFC strategy may be easily disturbed using several senses such as denial-of-service (DoS) intrusion [12][13][14], false-datainjection (FDI) attacks [15,16] resonance attacks [17,18], deception attacks [19], time-delay switch attacks [20], hybrid attacks [21], and load altering (LA) attacks [22]. Thus, to design an effective and reliable LFC scheme for CPPSs, any one of the counteractive strategies and security approaches can be considered. ...
... • Minimum time-characteristics are achieved simultaneously such as over/undershoot, settling time even in the presence of FDI and delay attack vulnerabilities in the LFC problem. • The performance of the proposed intelligent counter-active strategy is illustrated in terms of comparative responses with a generalized-extended-state-observer (GESO) based linear-quadratic regulator (LQR) [15]. ...
... Due to the presence of cyber-physical networks, several cyber-delay vulnerabilities, parametric uncertainties, and unmatched disturbances are present in LFC. These vulnerabilities are dependent on system nominal terms and states, and modeled as equivalent to a disturbance term Ω(x, t) as discussed in [15,23,24]. Thus, equivalent disturbance is defined. ...
he modern power grid has a severe issue known as unauthorized access through information technology-based devices. The performance and stability of the grid can deteriorate or even lead to destabilization under intentional manipulation of cyber-physical communication network statistics through injections of false information or delays. In this chapter, an intelligent artificial neural network (ANN) observer and robust sliding mode controller (SMC) are merged together to attenuate intelligently cyber-intrusions impact on the smart grid. Hence, a precise estimation scheme is implemented via intelligent observer in the presence of unknown inputs and delays vulnerabilities, and a counter control scheme is achieved using a nonlinear SMC. The feed-forward intelligent observer is used to provide a guaranteed estimation of uncertainties and as a result, the prediction error converges to zero asymptotically. In pursuance of minimum chattering in control efforts, predicted disturbances using an intelligent observer are used to determine SMC boundary limits. Furthermore, responses of an intelligent controller are compared with a state observer-based controller.
... Determineβ using (4e) 9: Using Nelder-Mead or another global optimization method 10: End the AGV due to different kinds of realistic cyberattacks [34]. These false data are injected within the system to ensure and demonstrate the performance of the proposed method in order to recognize the real and false states. ...
Automated Guided Vehicles (AGVs) have become a key part of many industries where they handle the task of managing material flows. As a result, they are very important targets for cyberattacks to cripple organizations by using methods such as False Data Injection (FDI) and Denial of Service (DoS) on sensors and measurement data. This paper introduces a new robust Kalman Filter (KF) for the AGV state estimation using Huber Loss Function. A statistical method known as M-estimation is used to solve the regression issue robustly and establish the equivalence between the Kalman filter and a specific least squares regression problem. For adaptive estimation of the unknown a priori state and observation noise statistics concurrently with the system states, M-robust estimators are developed. The proposed method tackles the state estimation issue against different kinds of cyber-attacks such as pulse, ramp, and DoS cyberattacks. The position estimation using constant velocity tracking based on KF employing with and without the robustification methods are compared. The results confirm the effectiveness and the robustness of the proposed Filter approach against the FDI due to the cyber-attacks compared with traditional Filters. Furthermore, the proposed method is investigated practically with Adlink-ROS AGV.
... However, the smart grid infrastructure can be disturbed by number of attacks so the system frequency and RCF must be within the range during whole operation (Ekanayake, J.B. et al., 2012;Butt, O.M. et al., 2021). The LFC system can be interrupted by different senses such as denial of service (DOS) attack (Li, Y. et al., 2019;Liu, S. et al., 2020;Davida, J. et al., 2020), delayed input attack (Mahmoud, M.S. et al., 2020), false data injection attack (FDIA) (Sargolzaei, A. et al., 2015;Aoufiab, S. et al., 2020) and other intrusions like load altering (LA) attacks (Prasad, S. 2020;Amini, S. et al., 2016;Wu, Y. et al., 2017) etc. A LFC system can be deteriorated by injecting false information by the attacker. ...
... Thus, smart grid infrastructure makes the system vulnerable so that a resilient LFC is required to protect the system from FDI attacks. In (Prasad, S. 2020;Amini, S. et al., 2016;Wu, Y. et al., 2017), the main focus is to design a mitigation scheme to protect the power system from load altering (LA) attack by using intelligent control technique in which both active defence and passive defence is able to control the LA attacks within tolerable frequency. The cyber intrusion is able to inject the unknown catastrophic disturbances through load altering or through area control error (ACE) signal via communication channel so the frequency can be easily disturbed and it must be in regulation range for a stable system. ...
... However, existing LFC control schemes may not be the foremost choice in word of feasibility and security. As results, few defensive controller (Liu, S. et al., 2020;Prasad, S. 2020;Amini, S. et al., 2016;Wu, Y. et al., 2017) are designed against cyber threats invasion but needed more intelligent detection and robust control strategy for more secure smart grid. ...
This study designs an effective mitigation control scheme for a class of attacks called resonance attacks on the power grids. Mainly in this attack an enemy is able to alter the input information of load frequency control (LFC) system according to feature of resonance frequency, for example rate of change of frequency (RCF) in a way that a LFC system becomes unstable via resonance frequency approach. Thus, in countermeasures of such type of cyber-attacks a detection and mitigation control scheme propose using artificial neural network (ANN) observer-based sliding mode controller (SMC) in this study. The malicious resonance offensive patterns are estimated via ANN observer effectively in terms of system uncertainties and it is used to control power system grid accurately. The asymptotic closed loop stability of proposed control scheme is analyzed using the Lyapunov stability theorem. The estimated states and uncertainties via ANN observer are utilized as input in SMC design to select switching surface boundary limits and minimize time characteristics. The presented control technique regulates rate of change of frequency within permissible limits. Therefore, proposed control technique is able to sustain closed loop system stability with reduction in chattering and frequency oscillations even in presence of resonance offensive patterns. The robustness of proposed mitigation scheme and its detection capability in terms of stability is illustrated by the MATLAB simulations. Index Terms-Load frequency control, cyber resonance attack, rate of change of frequency (RCF), artificial neural network observer, sliding mode controller.
... As a result, the transformer performance may distortion at non-economic conditions. There are various kinds of cyber-attacks versus numerical data for example; scaling attack, ramp attack, pulse attack, and random [73,74]. Regarding the scaling attack, this kind of attack utilizes a scaling factor to variate the real measurement signals. ...
The distribution of the power transformers at a far distance from the electrical plants represents the main challenge against the diagnosis of the transformer status. This paper introduces a new integration of an Internet of Things (IoT) architecture with deep learning against cyberattacks for online monitoring of the power transformer status. A developed one dimension convolutional neural network (1D-CNN), which is characterized by robustness against uncertainties, is introduced for fault diagnosis of power transformers and cyberattacks. Further, experimental scenarios are performed to confirm the effectiveness of the proposed IoT architecture. While compared to previous approaches in the literature, the accuracy of the new deep 1D-CNN is greater with 94.36 percent in the usual scenario, 92.58 percent when considering cyberattacks, and ±5% uncertainty. The proposed integration between the IoT platform and the 1D-CNN can detect the cyberattacks properly and provide secure online monitoring for the transformer status via the internet network.
... As a result, the system performance may degrade and force the system to operate at non-economical operating conditions due to non-optimal control signals or even lead to instability. There are different types of cyber attacks against numerical signals such as scaling attacks, ramp attacks, pulse attacks, and random attacks [29,30]. A scaling attack changes the true measurement signals to higher or lower values based on a scaling factor and a ramp attack changes the true measurement signals by the addition of a ramp factor. ...
This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity.
... GDB has a definite outline as the total magnitude of a continued speed change that there is no resulting variation in valve position. An observer-based control scheme is offered for LFC scheme against cyberattack uncertainties [31]. However, to test the effective response of the proposed controller of power networks, the GDB and GRC effect are not considered in the power plant. ...
... The simple configuration of the suggested GESO is presented in Fig. 4. It shows the uncertainties which can be designed and eliminated from the output channel in steady state by this control law. Fig. 4. The configuration of the proposed GESO [23][24][25], [30][31] ...
... lation 2. In last case, we consider the dynamic models Remark 6: The GRC and GDB impact significantly to feedback signal of the interconnected power network. To show the robustness of the proposed GESO, the simulation results are used to compare with the case of considering in [30] or without considering the GDB and GRC nonlinearity effects in [31]. The proposed controller clearly indicates that transient performance has adapted with required condition such as the setting time and under/overshoot in comparison with previous research. ...
This study investigates load frequency control based generalized extended state observer (GESO) for interconnected power systems subject to multi-kind of the power plant. First, the mathematical model of the interconnected power system is proposed based on the dynamic model of thermal power plant with reheat turbine and hydropower plant. Second, the GESO is designed to estimate the system states and disturbances. In addition, the problem of unmeasurable system states in the interconnected power network due to lack of sensor has been solved by using the proposed load frequency control based GESO. The numerical experiments are carried out by using MATLAB/ SIMULINK simulation. The simulation results point out that the proposed control approach has the capacity to handle the uncertainties and disturbances in the interconnected power system with better transient performances in comparison with the existing control approach. The relevant dynamic models have already been used for the simulation of the physical constraints of the governor dead band (GDB) and generation rate constraint (GRC) effect in the power plants. It is evident that the robustness of the suggested controller in terms of stability and effectiveness of the system. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
Monitoring ship emissions poses an intricate interdisciplinary challenge, encompassing atmospheric science, transportation, information communication, and computer technology. Key stakeholders in this effort include maritime institutions and shipping companies. However, effectively organizing information from ship emission monitoring, especially for policy implementation in the expansion of emission control areas (ECAs), faces substantial obstacles in data acquisition, transmission, analysis, and information services. To address these challenges, this study suggests an Internet of Things (IoT) paradigm using drones and deep learning for ship emission monitoring in the port areas, integrating vital technologies like industrial contact elements for IoT, assisted processing of ship emission, real-time data monitoring and online processing, and tailored information services for port control center. Additionally, the suggested IoT paradigm considers adversarial and false data injection (FDI) assaults, which impair the effectiveness of data processing and identification techniques. By actively supporting green port trends and maritime law enforcement, this new IoT paradigm is developing into the framework of the berth area inside the physical infrastructure. Furthermore, this model highlights the possibility for a more widespread adoption of ship pollution monitoring while simultaneously demonstrating the model’s efficacy in the current setting. A more expansive version of the suggested IoT paradigm might function as a strong cyber-physical infrastructure, supporting the capacity for thorough ship emission monitoring and enabling in-depth study on ship emissions.
This article demonstrates a research work on load frequency control (LFC) of a multi generating station based three area power system in concern to the Economic load dispatch (ELD). Both the action of LFC and ELD are obtained in a common platform with employing a noble fuzzy type-2 controller under a disturbance of step load perturbation of 2%. The controller has an advantages of highly accessing area since the concerned membership function creates a three dimensional structure as per its process. Further, this controller handles the huge uncertainty of power system smoothly due to its 3D structure. This enables to this controller for providing improved control performances as compared to the standard type-1 fuzzy controller and conventional PID controller. Since the outcomes of the controller largely depends upon the selection of proper parameter, this study has applied a novel hybridized harmony search and random search (hyHA-RS) technique for optimal gain formulation of this type-2 fuzzy controller. The optimal property of the suggested hyHS-RS techniques also has been examined over original random search and harmony search algorithms by simulated responses and results. Finally, it is concluded that applied hyHS-RS tuned T2-Fuzzy controller is much effective to obtain LFC and ELD in a common power system scenario.
