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Review of Cyberattack Implementation, Detection, and Mitigation Methods in Cyber-Physical Systems

ENERGIES (2023)

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Article Automation & Control Systems

Reliable control strategy based on sliding mode observer against FDI attacks in smart grid

Jian Li et al.

Summary: This paper proposes a method to ensure the reliable operation of cyber-physical systems under false data injection attacks. It designs a robust adaptive sliding mode observer to estimate the state of the power system and presents a method of attack reconstruction to estimate the actual attack signal. Finally, it proposes a reliable sliding mode control strategy to eliminate the impact of false data injection attacks.

ASIAN JOURNAL OF CONTROL (2023)

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Article Automation & Control Systems

Cyberattack-Resilient Secondary Frequency Control Scheme for Stand-Alone Microgrids

Anuoluwapo O. Aluko et al.

Summary: This article proposes a cyberattack-resilient scheme for secondary frequency control in a stand-alone microgrid system. The scheme accurately estimates the system states and maintains frequency stability under different cyberattack scenarios.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2023)

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Article Engineering, Electrical & Electronic

Novel SMO-Based Detection and Isolation of False Data Injection Attacks against Frequency Control Systems

Andrew D. Syrmakesis et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2023)

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Article Automation & Control Systems

Unknown input observer based distributed fault detection for nonlinear multi-agent systems with probabilistic time delay

Siyang Zhao et al.

Summary: This paper focuses on the fault detection problem for nonlinear continuous-time multi-agent systems with external disturbances and random time-varying delay. A stochastic variable is used to describe the random delay, and the external disturbances and faults are treated as unknown inputs. The proposed method can detect multiple faulty nodes simultaneously.

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2023)

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Article Engineering, Marine

Data-driven distributed formation control of under-actuated unmanned surface vehicles with collision avoidance via model-based deep reinforcement learning

Chao Pan et al.

Summary: This paper proposes a fully data-driven distributed control approach based on model-based deep reinforcement learning for multiple under-actuated unmanned surface vehicles (USVs) with fully unknown models, in order to achieve a desired formation and collision avoidance. The dynamic models of each USV are approximated by training a deep neural network with recorded input and output data. Model predictive formation controllers are then proposed to achieve safe formation control while considering collision avoidance. Simulation results demonstrate the feasibility and efficacy of the proposed method.

OCEAN ENGINEERING (2023)

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Article Automation & Control Systems

Temporal logic guided safe model-based reinforcement learning: A hybrid systems approach

Max H. Cohen et al.

Summary: This paper proposes a Model-Based Reinforcement Learning (MBRL) approach for synthesizing control policies from Linear Temporal Logic (LTL) specifications. Instead of using an abstraction-based approach, a hybrid automaton is introduced to model the interaction between the LTL formula's corresponding Buchi automaton and the nonlinear system. The proposed MBRL algorithm allows simultaneous learning of uncertain dynamics and value functions of each optimal control problem online while adhering to safety constraints based on Control Barrier Functions (CBFs).

NONLINEAR ANALYSIS-HYBRID SYSTEMS (2023)

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Article Automation & Control Systems

Cyber-Physical System-Based Path Tracking Control of Autonomous Vehicles Under Cyber-Attacks

Jinghua Guo et al.

Summary: This article focuses on the path tracking control problem of autonomous vehicles (AVs) under cyberattacks. The nonlinear state and measurement equations of AVs under cyberattacks are established based on the vehicle dynamics model. Sensor redundancy is introduced to improve the robustness of AVs against cyberattacks, and a cyberattack detection method using extended Kalman filter is designed. Sensor switching rules are developed to isolate the disturbance of cyberattacks. Model predictive control is used to formulate the control problem of AVs, and input-to-state stability of the control system under cyberattacks is established. Simulation results demonstrate the effectiveness of the proposed control strategy.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2023)

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Article Automation & Control Systems

Event-Driven Power Outage Prediction using Collaborative Neural Networks

Adeniyi K. Onaolapo et al.

Summary: An increase in weather events can lead to more grid faults and a decrease in electricity supply reliability. This article proposes a multiple-constraints event-driven outage model using the CONN algorithm to develop a better performance model for outage forecasting. The CONN algorithm successfully resolves the challenge of obtaining large complex weather events and outage data.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2023)

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Article Computer Science, Hardware & Architecture

Intelligent Diagnosis Using Continuous Wavelet Transform and Gauss Convolutional Deep Belief Network

Huimin Zhao et al.

Summary: This paper proposes a novel method for extracting vibration amplitude spectrum imaging features using continuous wavelet transform and image conversion, as well as a new CDBN for bearing fault classification. The proposed method outperforms traditional methods in performance, as shown in experiments on motor bearing datasets.

IEEE TRANSACTIONS ON RELIABILITY (2023)

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Article Automation & Control Systems

Extended sliding mode observer-based high-accuracy motion control for uncertain electro-hydraulic systems

Manh Hung Nguyen et al.

Summary: This paper presents a novel robust backstepping control strategy for achieving high-accuracy tracking performance in electro-hydraulic servo systems without velocity information, even in the presence of uncertainties and disturbances. The proposed approach includes the development of system dynamics, the establishment of extended sliding mode observers (ESMOs) for estimating the immeasurable variables, and the design of an observer-based controller. Numerical simulations demonstrate the advantages of the recommended method compared to existing control approaches.

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL (2023)

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Article Automation & Control Systems

Asymptotic convergence unknown input observer design via interval observer

Fanglai Zhu et al.

Summary: In this paper, a novelty unknown input observer (UIO) is proposed for a linear system with unknown input, which can achieve asymptotic system state estimation and unknown input reconstruction simultaneously via interval observer. The interval estimation of the output vector is obtained by designing an interval observer under the assumption that the boundary of the unknown input is known. Then, an algebraic relationship between the unknown input and the system state is established based on the upper and lower boundary estimations of the output vector. A Luenberger-like state observer together with an algebraic unknown input reconstruction is designed to form a UIO based on the algebraic relationship. Furthermore, a UIO is applied directly on a time-delay system with unknown input, demonstrating the advantages of the proposed UIO. Simulation examples and comparisons to existing results are provided to illustrate the effectiveness and advantages of the proposed method.

AUTOMATICA (2023)

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Article Energy & Fuels

Design and feasibility analysis of grid-connected hybrid renewable energy system: perspective of commercial buildings

T. Adefarati et al.

Summary: This paper proposes an HRES that consists of photovoltaics, electric vehicles, battery systems, and grids. The viability of the HRES is analyzed using data from Tucson Mall and NASA. The study demonstrates the importance of photovoltaic systems in reducing emissions, NPC, and COE, and improving the performance of conventional power systems.

ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS (2023)

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Review Computer Science, Theory & Methods

Cybersecurity of Industrial Cyber-Physical Systems: A Review

Hakan Kayan et al.

Summary: The increasing connectivity in Industrial Cyber-Physical Systems (ICPSs) has led to new vulnerabilities and security issues. Existing research mainly focuses on intrusion detection, network traffic analysis, and anomaly detection techniques, while neglecting the study of the outputs of industrial vulnerability assessment reports. This study defines and examines ICPSs from a cybersecurity perspective, presenting a multidimensional adaptive attack taxonomy to evaluate real-life ICPS cyber incidents.

ACM COMPUTING SURVEYS (2022)

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Article Energy & Fuels

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

Adeniyi Kehinde Onaolapo et al.

Summary: The reliability of the power supply depends on the reliability of the grid structure, which is prone to faults due to varying weather events. With the concern of increasing and severe weather events caused by climate change, it is important to explore predictive models for electricity outages caused by weather factors. This study presents a model using artificial neural networks to predict electricity outages caused by severe weather conditions and demonstrates their robustness compared to conventional models.

ENERGIES (2022)

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Article Computer Science, Artificial Intelligence

Multi-agent deep reinforcement learning: a survey

Sven Gronauer et al.

Summary: This article presents the recent advances in multi-agent deep reinforcement learning, focusing on training schemes for multiple agents, emerging behavior patterns in different scenarios, and challenges specific to the multi-agent domain along with the methods to address them. The survey discusses progress, identifies trends, and outlines potential future directions in this research area.

ARTIFICIAL INTELLIGENCE REVIEW (2022)

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Article Engineering, Electrical & Electronic

A Two-stage Kalman Filter for Cyber-attack Detection in Automatic Generation Control System

Ayyarao S. L. Tummala et al.

Summary: Communication is crucial in integrating smartness into the interconnected power system, but it is vulnerable to cyber-attacks. The proposed optimal two-stage Kalman filter (OTS-KF) effectively estimates state and cyber-attacks in the automatic generation control system, ensuring system security and reliability.

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY (2022)

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Review Computer Science, Information Systems

Two decades of cyberattack simulations: A systematic literature review

Viktor Engstrom et al.

Summary: Cyberattack simulations are a fragmented and inconsistent topic in computer security domains. This article presents the results of a systematic literature review, aiming to provide a unified baseline. The review shows that despite differences in implementation details, attack simulations share similar goals, contributions, and problem statements across different domains. However, constructing a fully unified view of the topic remains unclear. The results can guide practitioners and researchers in the field of attack simulations.

COMPUTERS & SECURITY (2022)

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Article Engineering, Electrical & Electronic

Optimized cyber-attack detection method of power systems using sliding mode observer

Mahdieh Adeli et al.

Summary: This paper investigates the problem of automatic detection of cyber-attacks in cyber-physical systems (CPSs), and proposes a new attack detector based on sliding mode observer (SMO) with parameter adjustment using an optimization algorithm. The experimental results demonstrate that the optimized attack detection scheme performs well in terms of detection accuracy and detection time.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Engineering, Civil

GeoTrackNet--A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection

Duong Nguyen et al.

Summary: Representing maritime traffic patterns and detecting anomalies are crucial for vessel monitoring and maritime situational awareness. The proposed GeoTrackNet utilizes state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks and a contrario detection to identify abnormal events. Experimental results on a real AIS dataset show the method's relevance compared to state-of-the-art schemes.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Article Green & Sustainable Science & Technology

Active and passive hybrid detection method for power CPS false data injection attacks with improved AKF and GRU-CNN

Zhaoyang Qu et al.

Summary: In this paper, an active and passive hybrid detection method for false data injection attacks (FDIAs) in power cyber-physical systems (CPS) with an improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed. The method addresses the limitations of traditional AKF algorithm and combines the advantages of neural networks to achieve accurate detection of attacks.

IET RENEWABLE POWER GENERATION (2022)

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Article Automation & Control Systems

A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges

Wenli Duo et al.

Summary: A cyber physical system integrates sensing, computation, control, and networking into physical processes and objects, playing a key role in modern industry. This paper surveys the state-of-the-art results of cyber attacks on cyber physical systems, discussing attack and defense strategies, as well as future challenges and open issues.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2022)

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Article Computer Science, Artificial Intelligence

A federated learning framework for cyberattack detection in vehicular sensor networks

Maha Driss et al.

Summary: Vehicular Sensor Networks (VSN) play a crucial role in modern transportation systems, but the increasing adoption of smart sensing technologies has made network security a major concern. This article proposes a Federated Learning (FL)-based attack detection framework for VSN, which has been proven to be highly efficient and accurate through experiments.

COMPLEX & INTELLIGENT SYSTEMS (2022)

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Article Automation & Control Systems

A Dynamic-State-Estimator-Based Tolerance Control Method Against Cyberattack and Erroneous Measured Data for Power Systems

Hassan Haes Alhelou et al.

Summary: This article proposes a dynamic-state-estimation-based cyberattack-tolerant control method for modern power systems. The method involves two new schemes for detecting and isolating cyberattacks, based on dynamic observer designs. The proposed method accurately tracks dynamic states and detects both cyberattacks and faulty measuring devices, showing superiority over other techniques.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Computer Science, Information Systems

Design of False Data Injection Attacks in Cyber-Physical Systems

Sushree Padhan et al.

Summary: Cyber-physical systems (CPSs) are prone to false data injection (FDI) attacks, which can be launched at any location in the CPS. This paper investigates the impact of FDI attacks at single and multiple locations on the security of CPS and validates their effectiveness through simulation examples.

INFORMATION SCIENCES (2022)

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Article Automation & Control Systems

Active multiplicative cyberattack detection utilizing controller switching for process systems

Shilpa Narasimhan et al.

Summary: This study analyzes multiplicative cyberattacks manipulating data in process control systems and proposes a novel active attack detection methodology to enhance the attack detection capabilities without significant degradation in the attack-free closed-loop performance.

JOURNAL OF PROCESS CONTROL (2022)

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Article Computer Science, Artificial Intelligence

Shuffle GAN With Autoencoder: A Deep Learning Approach to Separate Moving and Stationary Targets in SAR Imagery

Wei Pu

Summary: A novel shuffle GAN with autoencoder separation method is proposed for separating moving and stationary targets in SAR imagery, which effectively removes ambiguity and suppresses clutter in the images. Experiments on synthetic and real SAR data validate the effectiveness of the proposed method.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

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Article Computer Science, Information Systems

Real-Time Cyber Attack Detection Scheme for Standalone Microgrids

Anuoluwapo O. Aluko et al.

Summary: This article investigates false data injection attacks on frequency measurements in standalone microgrids and proposes an attack detection and identification method to protect the system from the impacts of such attacks. The robustness and practicality of the proposed method are demonstrated through real-time simulation results.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Article Engineering, Multidisciplinary

Detection and Prediction of FDI Attacks in IoT Systems via Hidden Markov Model

Hajar Moudoud et al.

Summary: This paper proposes a process for detecting and predicting false data injection (FDI) attacks in IoT systems. The process utilizes artificial intelligence to observe the behavior of IoT devices and predict their future actions, establishes trust between devices, and defends against attacks through bandwidth optimization and incentive mechanisms.

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING (2022)

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Article Engineering, Multidisciplinary

Optimal Design and Techno-Economic Analysis of a Grid-Connected Photovoltaic and Battery Hybrid Energy System

T. Adefarati et al.

Summary: The application of green energy technologies (GETs) has been universally accepted due to various factors such as the industrial revolution, increasing energy demand, high standard of living, population growth, and fluctuation of crude oil prices. This research focuses on optimizing the operation and design of a hybrid renewable energy system (HRES) by considering factors such as energy produced, cost of energy, return on investment, solar fraction, net present value, payback period, and CO2 emissions. The study compares the effects of different photovoltaic (PV) orientations on the technical, economic, and environmental performance of HRES and concludes that the two axes tracking system is the most feasible option.

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA (2022)

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Review Computer Science, Artificial Intelligence

Convolutional neural networks in medical image understanding: a survey

D. R. Sarvamangala et al.

Summary: This article discusses the importance of imaging techniques in capturing anomalies of the human body and the use of convolutional neural networks in medical image understanding. It also explores various tasks and applications in medical image diagnosis, focusing on the challenges and potentials of CNN in this field.

EVOLUTIONARY INTELLIGENCE (2022)

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Article Automation & Control Systems

Optimal ε -stealthy attack in cyber-physical systems

Weiwei Tu et al.

Summary: This paper discusses the optimal stealthy attack problems of cyber-physical systems, proposing some optimal attack strategies and demonstrating the validity of the theoretical results through numerical simulations.

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2021)

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Article Health Care Sciences & Services

Healthcare cyber-attacks and the COVID-19 pandemic: an urgent threat to global health

Menaka Muthuppalaniappan et al.

Summary: The COVID-19 pandemic has brought about widespread disruption in the healthcare industry, leading to an increase in cyber-security threats for healthcare organizations and universities. International and national regulatory bodies stress the urgent need for protection against cyber-attacks during this period.

INTERNATIONAL JOURNAL FOR QUALITY IN HEALTH CARE (2021)

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Article Computer Science, Information Systems

Detecting Cyber Attacks in Smart Grids Using Semi-Supervised Anomaly Detection and Deep Representation Learning

Ruobin Qi et al.

Summary: Smart grids incorporate advanced ICTs for efficient power delivery and management, but also introduce new security vulnerabilities. This paper presents a new method for detecting cyber attacks in smart grids using semi-supervised anomaly detection and deep representation learning, which outperforms popular supervised algorithms in finding attack events. Results show that the performance of semi-supervised anomaly detection algorithms can be further enhanced by incorporating deep representation learning.

INFORMATION (2021)

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Article Computer Science, Information Systems

Using data mining techniques to explore security issues in smart living environments in Twitter

Jose Ramon Saura et al.

Summary: The study aimed to explore the main security issues in smart living environments by conducting sentiment analysis and topic classification on tweets, followed by extracting insights and statistical information. It was found that the main security issues include malware, cybersecurity attacks, data storing vulnerabilities, the use of testing software in IoT, and possible leaks due to the lack of user experience.

COMPUTER COMMUNICATIONS (2021)

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Article Computer Science, Artificial Intelligence

Deep neural network models for computational histopathology: A survey

Chetan L. Srinidhi et al.

Summary: This paper presents a comprehensive review of state-of-the-art deep learning approaches used in histopathological image analysis. Through a survey of over 130 papers, the progress in the field based on different machine learning strategies is reviewed. Additionally, the paper discusses the application of deep learning in survival models and highlights the challenges and limitations of current deep learning methods, as well as potential directions for future research.

MEDICAL IMAGE ANALYSIS (2021)

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Article Automation & Control Systems

Secure State Estimation and Control of Cyber-Physical Systems: A Survey

Derui Ding et al.

Summary: Cyber-physical systems integrate physical processes and cyber infrastructure with the help of computational resources and communication capabilities for extensive applications. However, the security of CPSs is a major concern due to vulnerabilities from the tight integration of cyber and physical components, necessitating reliable monitoring and operation techniques.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

Electrical load forecasting: A deep learning approach based on K-nearest neighbors

Yunxuan Dong et al.

Summary: This study proposes a deep learning approach based on K-nearest neighbors to address the high computing costs and improve accuracy of electrical load forecasting. Experimental results demonstrate that the proposed method can enhance both forecasting efficiency and accuracy while simplifying the forecasting process.

APPLIED SOFT COMPUTING (2021)

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Article Engineering, Electrical & Electronic

Distributed Sliding Mode Observer-Based Secondary Control for DC Microgrids Under Cyber-Attacks

Yajie Jiang et al.

Summary: A distributed sliding mode observer-based secondary control method is proposed in this paper to enhance the stability and security of distributed energy resources in DC microgrids. By detecting false signals and compensating control variables, the proposed method effectively regulates DER systems under different types of cyber-attacks, as verified through simulation and experimental results.

IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS (2021)

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Article Energy & Fuels

A Case Study of an Industrial Power Plant under Cyberattack: Simulation and Analysis

Marilena Stanculescu et al.

Summary: This study examines the impact of cyberattacks on power systems, focusing on vulnerabilities and changes following the attacks. By simulating cyberattacks on electrical equipment in a petrochemical plant and analyzing the resulting faults and parameter changes, the resilience of the system can be assessed.

ENERGIES (2021)

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Article Engineering, Electrical & Electronic

Cyber Security in Control of Grid-Tied Power Electronic Converters-Challenges and Vulnerabilities

Subham Sahoo et al.

Summary: Grid-tied power electronic converters play a key role in connecting renewable energy sources, energy storage, electrical vehicles, microgrids, and high-voltage dc transmission lines to the electrical power grid. However, as the number of converters in modern grids increases, their vulnerability to cyber-attacks is a growing concern. Recent standards have defined a mandatory set of control parameters that can be adjusted remotely through a communication network.

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS (2021)

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Article Computer Science, Artificial Intelligence

Large group activity security risk assessment and risk early warning based on random forest algorithm

Yanyu Chen et al.

Summary: This paper evaluates and warns security risks of large-scale group activities based on the random forest algorithm, achieving a maximum classification accuracy rate of 0.86 through model optimization and training experiments, demonstrating the algorithm's strong predictive ability in risk assessment.

PATTERN RECOGNITION LETTERS (2021)

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Article Engineering, Electrical & Electronic

A Unified Architectural Approach for Cyberattack-Resilient Industrial Control Systems

Chunjie Zhou et al.

Summary: This article presents a global and systematic architectural approach for industrial control system cybersecurity to address the threats posed by cyberattacks on ICSs. Through the integration of secure networks, secure control systems, and secure physical processes, layer-by-layer defense is implemented to enhance the network security risk management level of ICSs.

PROCEEDINGS OF THE IEEE (2021)

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Review Chemistry, Analytical

Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap

Lubna Luxmi Dhirani et al.

Summary: Industrial Internet of Things (IIoT) is a new concept aiming to improve manufacturing processes and efficiency through a fully connected, transparent, automated, and intelligent factory setup. However, the transition from existing models to a vertical model and the implementation of new communication technologies pose challenges in achieving desired levels of data security.

SENSORS (2021)

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Review Energy & Fuels

A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments

Yuchong Li et al.

Summary: Most economic, commercial, cultural, social, and governmental activities worldwide take place in cyberspace, with organizations facing challenges from cyber-attacks and wireless communication technology dangers. Various solutions are used to protect data, and researchers propose methods to prevent or minimize damage from cyber-attacks.

ENERGY REPORTS (2021)

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Proceedings Paper Energy & Fuels

Forecasting Electricity Outage in KwaZulu-Natal, South Africa using Trend Projection and Artificial Neural Networks Techniques

A. K. Onaolapo et al.

Summary: The majority of faults on electric power systems are traceable to distribution networks due to exposure to adverse climatic conditions. It is important to consider climatic factors when designing a power system model for meaningful system reliability improvement. This research proposes a computational intelligence model using historical fault and climatic data sets, which showed satisfactory results in reducing power outages.

2021 IEEE PES/IAS POWERAFRICA CONFERENCE (2021)

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Article Computer Science, Information Systems

A Survey of Cyberattack Countermeasures for Unmanned Aerial Vehicles

Peng-Yong Kong

Summary: This paper surveys existing literature on UAV cyberattacks, classifying them into six types and reviewing corresponding countermeasure. Countermeasures are classified into prevention, detection, and mitigation categories to help UAVs counter both existing and new cyberattacks.

IEEE ACCESS (2021)

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Proceedings Paper Computer Science, Information Systems

Training regime influences to semi-supervised learning for insider threat detection

Duc C. Le et al.

Summary: This paper presents a semi-supervised learning approach to Insider threat detection which allows learning from very limited data at high precision, effectively detecting malicious data instances.

2021 IEEE SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (SPW 2021) (2021)

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Article Automation & Control Systems

A survey on attack detection, estimation and control of industrial cyber-physical systems

Dan Zhang et al.

Summary: The paper surveys recent advances on security issues of industrial cyber-physical systems (ICPSs), focusing on two typical types of attacks - Denial-of-Service (DoS) attack and Deception attack. Recent results on attack detection, estimation, and control of ICPSs are presented. Current studies are classified and summarized based on different system modeling and analysis methods, discussing advantages and disadvantages of various methodologies. Potential future research directions on secure ICPSs are also highlighted.

ISA TRANSACTIONS (2021)

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Proceedings Paper Energy & Fuels

A Laboratory Set-Up for Cyber Attacks Simulation Using Protocol Analyzer and RTU Hardware Applying Semi-Supervised Detection Algorithm

Ali Parizad et al.

Summary: This paper proposes a method to simulate and detect injected false data in cyberattacks, using a laboratory setup and supervised learning algorithm. The results show the effectiveness of this method in detecting FDIA.

2021 IEEE TEXAS POWER AND ENERGY CONFERENCE (TPEC) (2021)

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Article Computer Science, Information Systems

Building an Intrusion Detection System to Detect Atypical Cyberattack Flows

Ulya Sabeel et al.

Summary: This paper proposes a defensive AI engine combined with a twofold feature selection technique and hyperparameter optimization of the AI model for binary attack flow identification. The system significantly improves the True Positive Rate (TPR) of AI models on multiple atypical attacks, demonstrating its effectiveness in detecting unusual attack flows using various Deep Learning and Machine Learning models.

IEEE ACCESS (2021)

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Review Computer Science, Cybernetics

Observer-Based Attack Detection and Mitigation for Cyberphysical Systems A Review

Mojtaba Kordestani et al.

IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE (2021)

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Article Computer Science, Information Systems

A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems

Sanaa Kaddoura et al.

Summary: In the era of the Internet of things, companies have transitioned from paper-based data to electronic formats, with healthcare databases being a prime target for attackers. This paper introduces a novel approach inspired by COVID-19 infection behavior to assess database damage in healthcare systems efficiently. The proposed parallel algorithm significantly outperforms existing algorithms in terms of both time and memory, providing a minimized execution time.

FUTURE INTERNET (2021)

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Article Psychology, Mathematical

A tutorial on Bayesian multi-model linear regression with BAS and JASP

Don van den Bergh et al.

Summary: Linear regression analysis commonly involves two stages: defining the best model and using regression coefficients for prediction and evaluation; traditional inference methods often ignore model uncertainty, leading to overconfident parameter estimates. Model averaging is a technique that overcomes these drawbacks by weighting the contribution of each model for inference.

BEHAVIOR RESEARCH METHODS (2021)

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Article Computer Science, Information Systems

Improving the Prediction of Heart Failure Patients' Survival Using SMOTE and Effective Data Mining Techniques

Abid Ishaq et al.

Summary: The study analyzed heart failure patient survival data, applied multiple classification models for prediction, and ultimately achieved the best accuracy with Extra Tree Classifier in data processing and training stages.

IEEE ACCESS (2021)

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Article Computer Science, Interdisciplinary Applications

An overview and comparison of supervised data mining techniques for student exam performance prediction

Nikola Tomasevic et al.

COMPUTERS & EDUCATION (2020)

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Article Automation & Control Systems

Sensor attack detection and isolation based on sliding mode observer for cyber-physical systems

Lei Ye et al.

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING (2020)

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Article Engineering, Electrical & Electronic

Deterministic Dynamic State Estimation-Based Optimal LFC for Interconnected Power Systems Using Unknown Input Observer

Hassan Haes Alhelou et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

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Article Engineering, Electrical & Electronic

A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

Ahmed S. Musleh et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

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Article Computer Science, Information Systems

Detection and Isolation of False Data Injection Attacks in Smart Grid via Unknown Input Interval Observer

Xinyu Wang et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

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Article Automation & Control Systems

Summation Detector for False Data-Injection Attack in Cyber-Physical Systems

Dan Ye et al.

IEEE TRANSACTIONS ON CYBERNETICS (2020)

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Article Automation & Control Systems

False data injection attacks with complete stealthiness in cyber-physical systems: A self-generated approach

Tian-Yu Zhang et al.

AUTOMATICA (2020)

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Article Engineering, Electrical & Electronic

Focusing Matching Localization Method Based on Indoor Magnetic Map

Gong-Xu Liu et al.

IEEE SENSORS JOURNAL (2020)

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Article Computer Science, Information Systems

Secure Consensus Control of Multiagent Cyber-Physical Systems With Uncertain Nonlinear Models

Hamed Rezaee et al.

IEEE SYSTEMS JOURNAL (2020)

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Article Nuclear Science & Technology

Robust localized cyber-attack detection for key equipment in nuclear power plants

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PROGRESS IN NUCLEAR ENERGY (2020)

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