Security Of Control Systems Research Articles

Influence of the Configuration of Airport Security Control Systems on the Implementation of Assumptions of the Sustainable Development Policy

Influence of the Configuration of Airport Security Control Systems on the Implementation of Assumptions of the Sustainable Development Policy

Event-Based Finite-Time $$H_\infty $$ Security Control for Networked Control Systems with Deception Attacks

Event-Based Finite-Time $$H_\infty $$ Security Control for Networked Control Systems with Deception Attacks

Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling—An Oil and Gas Sector Case Study

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.

Co-Design of Dynamic Event-Observer-Based Security Control for Interconnected Nonlinear Power Systems Under Actuator Deception Attacks

Co-Design of Dynamic Event-Observer-Based Security Control for Interconnected Nonlinear Power Systems Under Actuator Deception Attacks

An improved smart contract-based bring your own device (BYOD) security control framework

The evolution of mobile technology has produced new methods and policies for organisations to process data and communicate. Bring your own device (BYOD), which allows employees to bring their own personal devices to work and access organisational resources for work purposes, is one such new policy. However, as this practice poses significant risks, organisations must implement commensurate security measures to protect their integrity. This paper aimed to mitigate these risks by proposing a decentralized and unassailable security control solution tailored to the BYOD environment. The proposed architecture leveraged business blockchain with smart contracts (SCs) to automate policy compliance and strictly adhere to organisational rules and regulations. The research demonstrated that this approach effectively reduces access control (AC) threats and enhances security policies and management. The findings highlighted that the implementation of SCs within a blockchain framework significantly improves the security of the BYOD environment by minimising the risks of unauthorised access, data breaches, and insider threats. Additionally, SCs enable organisations to establish decentralised and tamper-proof security control systems, reducing the dependence on centralised authorities and bolstering overall system integrity.

Adaptive event-triggered security control for networked teleoperation systems.

An adaptive event-triggered security control method is proposed for networked robotic teleoperation systems subject to time-varying delays and false data injection (FDI) attacks. An event-triggered scheme is designed via the position and velocity signals of the master and slave robots, where the triggering thresholds can change adaptively with the system states. The position and velocity triggered signals and the feedback error between the operator and the environment forces are utilized to detect whether the transmitted signals suffered from FDI attacks. Then, a switching controller is designed, which is able to adopt the corresponding control strategy according to the corresponding detection result. The stability of the system and the convergence of the tracking errors are proved through Lyapunov functions, and the proposed method is validated through practical experiments. The proposed method is able to adaptively adjust the triggering frequency and effectively reduce the transmitted data, thus saving the network resources. Meanwhile, it can ensure the stability of networked teleoperation systems under time-varying delays and FDI attacks, as well as the position and force tracking performance.

A Survey of Cyber Security and Safety in Industrial Control Systems

A Survey of Cyber Security and Safety in Industrial Control Systems

Output feedback adaptive inverse optimal security control for stochastic nonlinear cyber-physical systems under sensor and actuator attacks

Output feedback adaptive inverse optimal security control for stochastic nonlinear cyber-physical systems under sensor and actuator attacks

Adaptive fuzzy backstepping secure control for incommensurate fractional order cyber–physical power systems under intermittent denial of service attacks

This paper proposes a fuzzy adaptive control strategy, based on backstepping, for incommensurate fractional-order power systems under denial-of-service (DoS) attacks. To address a wider range of fractional-order systems, this study assumes that the agents’ dynamics where each state have non-identical fractional derivative orders. This assumption brings up additional complexity to the design of the secure control system compared to existing approaches. Our approach tackles the challenge of maintaining system stability during both normal and attack periods by integrating adaptive control technique. The method includes an attack compensator to manage unavailable output measurements during attacks and a fuzzy estimator to approximate unobservable states. It also employs a fractional-order adaptive law and a backstepping controller to ensure performance and security, maintaining bounded tracking errors. MATLAB simulations confirm the effectiveness and robustness of the proposed method, demonstrating its ability to protect power systems from cyber threats while ensuring operational integrity and stability. Compared to prior studies, the presence of an attack compensator reduces both overshoots and convergence time following each attack.

Face Detection and Recognition for Criminal Identification System

This project showcases a powerful Face Detection and Recognition system intended to improve law enforcement skills by speeding up the identification of suspects in criminal cases. The system uses scalable databases and real-time computation, however instead of using traditional web sources for face detection. Accuracy and dependability are ensured by using the LBPH algorithm for face recognition and the Haar-cascade classifier for feature detection. The smtplib library allows the system to send a Gmail notification when it finds a matching face. The system that has been built contributes to public safety initiatives by improving law enforcement operations and demonstrating versatility for different face detection circumstances. The outcomes show how successfully the system can detect faces in real-time circumstances. The system achieves great performance by creating dynamic datasets and integrating algorithms. Integrating Gmail improves law enforcement response by facilitating prompt contact of identified suspects. This study highlights the role that surveillance technology plays in public safety and crime prevention, and it provides law enforcement authorities with useful solutions. Furthermore, the uses of this facial recognition and detection technology go beyond police enforcement. It can expedite attendance tracking and improve security in access control systems. Because of its adaptability, it can be used in a variety of industries and is expected to have a significant impact.

Secure and transparent traffic congestion control system for smart city using a federated learning approach

This study addresses the increasing problems of traffic congestion in smart cities by introducing a Secure and Transparent Traffic Congestion Control System using federated learning. Traffic congestion control systems face key issues such as data privacy, security vulnerabilities, and the necessity for joint decision-making. Federated learning, a type of distributed machine learning, is effective because it allows for training models on decentralized data while maintaining data privacy. Furthermore, incorporating blockchain technology improves the system’s security, integrity, and transparency. The proposed system uses federated learning to securely gather and analyze local traffic data from different sources within a smart city without moving sensitive data away from its original location. This method minimizes the risk of data breaches and privacy issues. Blockchain technology creates a permanent, transparent record for monitoring and confirming decisions related to traffic congestion control, thereby promoting trust and accountability. The combination of federated learning's decentralized nature and blockchain's secure, transparent features aids in building a strong traffic management system for smart cities. This research contributes to advancements in smart city technology, potentially improving traffic management and urban living standards. Moreover, tests of the new combined model show a high accuracy rate of 97.78% and a low miss rate of 2.22%, surpassing previous methods. The demonstrated efficiency and adaptability of the model to various smart city environments and its scalability in expanding urban areas are crucial for validating its practical use in real-world settings.

Based on the Security Control of Pumped Storage Hydropower under Extreme Fault Conditions after Grid Connection

Abstract As an important part of the new energy grid connection, pumped storage hydropower plays an important role in black start, frequency modulation and phase modulation, peak shaving and valley filling, energy storage and accident reserve. In order to ensure the security and stability of the power system under extreme fault conditions after the pumped storage hydropower is connected to the grid, it is particularly important to configure reasonable security and stability control measures. Based on the development status and operation characteristics of pumped storage hydropower stations at home and abroad, considering the need of system security and stability control, this paper takes a pumped storage hydropower station in Southwest China as an example to simulate and analyze the transient characteristics of the pumped storage hydropower after grid connection. In view of the possible extreme fault conditions, a regional security control system is designed, and the configuration, control measures and strategies of the security control device on the pumping side are studied.

Dynamic memory-based event-triggered security control for networked control systems subjected to false-injection-data attack

This study addresses the security control problem for networked control systems that are subjected to false-data-injection attacks. The designed controller rests on a dynamic memory-based event-triggered mechanism, which exploits historically transmitted data and an auxiliary term to improve transmission efficiency and ensure satisfactory control performance. Then, a controller that defends against the false-data-injection attacks is proposed by making full use of the historical information. With the aid of Cauchy–Schwarz and Gronwall–Bellman inequalities, the existence of such a controller is validated through a novel Lyapunov-based method, which allows the Lyapunov function to be nonpositive. Further, a test on a dynamic power system is provided to verify the effectiveness and superiority of the proposed method.

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring.

Secure pharmaceutical supply chain using blockchain in IoT cloud systems

Supply Chain Management (SCM) systems require time sequencing, coordination and tracking the movement of goods and processes. Internet of Things (IoT) and Blockchain technologies are useful to develop a secure automated SCM. IoT devices with in-built sensors and actuators help to keep track of the state, location, temperature or other parameters of an entity, and control the automation of routine as well as hazardous tasks. Blockchain technology supports time-stamping, authentication, process coordination, non-repudiation, commercial transactions, and also provides security for transactions and storage. The pharmaceutical SCM demands accurate, immediate and secure control system. Additionally, the supply chain process data from IoTs is stored and processed in Cloud by Analytics applications, for business planning and improvement. An efficient and secure IoT-Cloud-Blockchain based system for both SCM automation and analytics has been proposed in this work. It leverages a hierarchical IoT, Mist, Edge, Fog, Cloud computing (IMEFC) architecture to enhance Communication-Response-Compute-Security-Storage (CRCSS) in the system. Blockchain technology provides security for the SCM transactions and data. The efficiency of the Blockchain is measured in terms of upload time, download time and transaction fees for Bitcoin, Ethereum and Filecoin platforms. The Filecoin blockchain platform is quicker and cost-effective for larger file sizes, compared to Ethereum and Bitcoin, making it suitable for Pharmaceutical SCM systems.

REAL TIME FACE ATTENDANCE SYSTEM USING DEEP LEARNING

One's face is a physical depiction of who they are. As a result, we have suggested a facial recognition- based automatic student attendance system. The usage of face recognition technology in everyday life is particularly beneficial for security control systems. Face recognition is used by the FBI (Federal Bureau of Investigation) for criminal investigations as well as the airport security system to identify criminals. In the first step of our suggested method, video framing, the camera is activated using a user-friendly interface. The Viola-Jones method is used to identify and segment the face ROI from the video frame. If necessary, image size scaling is done at the pre-processing step to prevent information loss. After using the median filter to reduce noise, color pictures are transformed into grayscale versions. Then, to improve the contrast of the pictures, contrast-limited adaptive histogram equalization (CLAHE) is applied. Principal component analysis (PCA) and improved local binary pattern (LBP) are used appropriately in the face identification stage to extract the characteristics from facial pictures. By minimizing the lighting impact and boosting identification rates, the improved local binary pattern outperforms the original local binary pattern in our suggested method. The features that were retrieved from the training pictures and the features that were extracted from the test images are then compared. The face pictures are then identified and categorized using the upgraded LBP, PCA, and algorithm that produced the best results. The acknowledged student's attendance will then be recorded and saved in the excel file. A warning will be sent if a student signs in more than once. The student who is not enrolled will also be allowed to register right away. When two photographs per person are trained, the average recognition accuracy is 100% for high-quality images, 94.12% for low-quality images, and 95.76% for the face database.

Enhancing Data Preservation and Security in Industrial Control Systems through Integrated IOTA Implementation

Within the domain of industrial control systems, safeguarding data integrity stands as a pivotal endeavor, especially in light of the burgeoning menace posed by malicious tampering and potential data loss. Traditional data storage paradigms, tethered to physical hard disks, are fraught with inherent susceptibilities, underscoring the pressing need for the deployment of resilient preservation frameworks. This study delves into the transformative potential offered by distributed ledger technology (DLT), with a specific focus on IOTA, within the expansive landscape of the Internet of Things (IoT). Through a meticulous examination of the intricacies inherent to data transmission protocols, we present a novel paradigm aimed at fortifying data security. Our approach advocates for the strategic placement of IOTA nodes on lower-level devices, thereby streamlining the transmission pathway and curtailing vulnerabilities. This concerted effort ensures the seamless preservation of data confidentiality and integrity from inception to storage, bolstering trust in the convergence of IoT and DLT technologies. By embracing proactive measures, organizations can navigate the labyrinthine terrain of data management, effectively mitigate risks, and cultivate an environment conducive to innovation and progress.

Dynamic analysis based on a memristive hyperchaotic system with stable unfixed point and its synchronization application

A novel two-memristor hyperchaotic system is obtained by introducing a cubic magnetic-controlled memristor and a hyperbolic sine function memristor. The dynamics of the new system are analyzed by various techniques such as Lyapunov exponents, complexity, 0–1 test, bifurcation diagram and phase diagram. The results demonstrate that the new system exhibits complex dynamic behaviors, including transient chaos, transient transition, intermittent chaos, and offset-boosting. Notably, a rare phenomenon with stable unfixed point has been discovered in this newly proposed system. The largest Lyapunov exponent of the stable unfixed point fluctuates around 0 and remains predominantly less than or equal to 0. Despite this, the new system still partially exhibits chaotic characteristics, indicating that the stable unfixed point can be regarded as a local chaotic attractor. Furthermore, there are four types of coexisting attractors with period-period, chaos-chaos, chaos-stable unfixed point and stable unfixed point-stable unfixed point in the new system. The circuit design is implemented to validate the accuracy of the memristive chaotic system, and the consistency between numerical calculations and simulation results is confirmed. Finally, the coupling synchronization and tracking synchronization methods are designed, which hold practical applications in the field of secure communication, control systems and signal processing.

Feasibility of Using Seq-GAN Model in Vulnerability Detection of Industrial Control Protocols

Continuous improvement of internet technology has driven the continuous progress and improvement of industrial control systems, and provided more support for security vulnerability detection in this field. Combining the GAN model, the detection model based on Seq-GAN in industrial control protocol vulnerabilities constructed in this article provides more options for further improving the security of industrial control systems, and can detect and analyse security vulnerabilities in industrial control protocols more efficiently and accurately. By comparing the performance of different models for security vulnerability detection, the Seq-GAN model has smaller prediction errors, can also obtain higher G-mean and F1-score values, and has sufficient reliability. At the same time, it can also improve the efficiency of vulnerability detection in industrial control systems, and can achieve better comprehensive detection performance. Therefore, the application of the Seq-GAN model in industrial control protocol vulnerability detection can provide more support for improving security detection in this field.

인공지능 기반의 자동화된 통합보안관제시스템 모델 연구

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.