Cyber-Physical Systems Systems Research Articles

The Evolution Mechanism of Correctness for Cyber-Physical System

Cyber-physical Systems (CPS) are widely used in all areas of life. Ensuring the correctness of CPS remains an enduring challenge during its development and design phases. One approach to ascertain the correctness of CPS is behavior equivalence based on bisimulation. However, whether the implementation development process develops in the right direction has an important impact on obtaining the correct system implementation. Especially, the development process of complex CPS often yields a multitude of implementation versions, the correlation among these versions partly signifies the correctness of the development process. This paper formalizes the relationship between the implementation versions and establishes a formal description of the development process progressing in the correct direction, leveraging the concept of a “limit idea.” Initially, we introduce the concept of “limit bisimulation” for CPS systems to delineate implementations acquired during the CPS development process. Specific limit bisimulations are demonstrated to represent distinct scenarios within the development process. Subsequently, the convergence mechanism of implementations is modeled through bisimulation limits, encapsulating the CPS’s specification as the ultimate goal of obtained implementations during the development process. Finally, the congruence property of the bisimulation limit is proved, which accounts for the relation between specification and implementation versions can be decomposed into a refinement hierarchy. The limit theorem of bisimulation in CPS can help the designer and developer of CPS to comprehend the development course better.

An adaptive predefined time sliding mode control for uncertain nonlinear cyber-physical servo system under cyber attacks

Malicious attacks are often inevitable in cyber-physical systems (CPS). Accuracy in Cyber physical system for position tracking of servos is the major concern now a days. In high precision industrial automation, it is very hard to achieve accuracy in tracking especially under malicious cyber-attacks, control saturations, parametric perturbations and external disturbances. In this paper, we have designed a novel predefined time (PDT) convergence sliding mode adaptive controller (PTCSMAC) for such kind of cyber physical control system. Main key feature of our control is to cope these challenges that are posed by CPS systems such as parameter perturbation, control saturation, and cyber-attacks and the whole system then upgrade to a third-order system to facilitate adaptive control law. Then, we present an adaptive controller based on the novel PDT convergent sliding mode surface (SMS) combined with a modified weight updated Extreme Learning Machine (ELM) which is used to approximate the uncertain part of the system. Another significant advantage of our proposed control approach is that it does not require detailed model information, guaranteeing robust performance even when the system model is uncertain. Additionally, our proposed PTCSMAC controller is nonsingular regardless of initial conditions, and is capable of eradicating the possibility of singularity problems, which are frequently a concern in numerous CPS control systems. Finally, we have verified our designed PTCSMAC control law through rigorous simulations on CPS seeker servo positioning system and compared the robustness and performance of different existing techniques.

Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs.

Status, challenges, and promises of data‐driven battery lifetime prediction under cyber‐physical system context

AbstractEnergy storage is playing an increasingly important role in the modern world as sustainability is becoming a critical issue. Within this domain, rechargeable battery is gaining significant popularity as it has been adopted to serve as the power supplier in a broad range of application scenarios, such as cyber‐physical system (CPS), due to multiple advantages. On the other hand, battery inspection and management solutions have been constructed based on the CPS architecture in order to guarantee the quality, reliability and safety of rechargeable batteries. In specific, lifetime prediction is extensively studied in recent research as it can help assess the quality and health status to facilitate the manufacturing and maintenance. Due to the aforementioned importance, the authors aim to conduct a comprehensive survey on the data‐driven techniques for battery lifetime prediction, including their current status, challenges and promises. In contrast to existing literature, the battery lifetime prediction methods are studied under CPS context in this survey. Hence, the authors focus on the algorithms for lifetime prediction as well as the engineering frameworks that enable the data acquisition and deployment of prediction models in CPS systems. Through this survey, the authors intend to investigate both academic and practical values in the domain of battery lifetime prediction to benefit both researchers and practitioners.

An equilibrium optimizer with deep recurrent neural networks enabled intrusion detection in secure cyber-physical systems

<abstract> <p>Cyber-physical systems (CPSs) are characterized by their integration of physical processes with computational and communication components. These systems are utilized in various critical infrastructure sectors, including energy, healthcare, transportation, and manufacturing, making them attractive targets for cyberattacks. Intrusion detection system (IDS) has played a pivotal role in identifying and mitigating cyber threats in CPS environments. Intrusion detection in secure CPSs is a critical component of ensuring the integrity, availability, and safety of these systems. The deep learning (DL) algorithm is extremely applicable for detecting cyberattacks on IDS in CPS systems. As a core element of network security defense, cyberattacks can change and breach the security of network systems, and then an objective of IDS is to identify anomalous behaviors and act properly to defend the network from outside attacks. Deep learning (DL) and Machine learning (ML) algorithms are crucial for the present IDS. We introduced an Equilibrium Optimizer with a Deep Recurrent Neural Networks Enabled Intrusion Detection (EODRNN-ID) technique in the Secure CPS platform. The main objective of the EODRNN-ID method concentrates mostly on the detection and classification of intrusive actions from the platform of CPS. During the proposed EODRNN-ID method, a min-max normalization algorithm takes place to scale the input dataset. Besides, the EODRNN-ID method involves EO-based feature selection approach to choose the feature and lessen high dimensionality problem. For intrusion detection, the EODRNN-ID technique exploits the DRNN model. Finally, the hyperparameter related to the DRNN model can be tuned by the chimp optimization algorithm (COA). The simulation study of the EODRNN-ID methodology is verified on a benchmark data. Extensive results display the significant performance of the EODRNN-ID algorithm when compared to existing techniques.</p> </abstract>

A Data Analysis Framework Based on Cyber–Physical Systems to Support Data-Driven Decision-Making for Construction Sustainability

The construction industry has seen significant advances with the rapid development of cyber–physical systems (CPS). However, the dynamic and complex nature of construction projects still presents various engineering problems throughout their lifecycle. Traditional data analysis methods for engineering problems are inefficient due to the difficulty of collecting, processing, and aggregating data from disparate CPS systems. The lack of practical data analysis methods for solving engineering problems severely affects the sustainability of construction projects. This article develops a CPS-based data analysis framework, the spatial, temporal, and dynamic dimensional data analysis framework, which integrates the data analysis process, decision-making methods, and CPS technologies. It aims to facilitate more efficient data-driven decision-making on engineering issues to improve the sustainability of construction projects. The proposed framework has been implemented in a case study of the Shanghai Planetarium project to demonstrate its applicability and effectiveness.

Securing Wireless Communication in Cyber-Physical Systems and the Internet of Things: Addressing Security Challenges

Safeguarding wireless communication in cyber-physical systems (CPS) and the Internet of Things (IoT) is important to stop hackers, data breaches, and system interruptions. Multiple security risks can affect these systems because they are linked together and depend on wireless communication methods. This essay looks at the security issues that mobile and internet-connected things (IoT) have and suggests ways to fix them. Two of the biggest problems are keeping data sent over wifi networks private and secure. You can keep data safe from people listening in or changing it by using encryption methods like Advanced Encryption Standard (AES) and Rivest Cipher (RC4). Furthermore, security tools like Public Key Infrastructure (PKI) and Digital Signatures can confirm the authenticity of devices and make sure that contact is safe. An additional problem is that radio communication methods can be attacked with tactics like man-in-the-middle and repeat attacks. You can make CPS and IoT systems safer by using secure methods like Transport Layer Security (TLS) and Secure Shell (SSH). Protecting radio contact is also important to avoid problems caused by denial-of-service (DoS) attacks. Employing intrusion detection systems (IDS) and firewalls can assist in finding and blocking harmful traffic, ensuring that communication stays unbroken. Lastly, protecting radio transmission in CPS and IoT is important to avoid many security risks. The security of CPS and IoT systems can be improved by using encryption methods, identification mechanisms, and safe protocols to keep data private, secure, and accessible.

A Low-Code Approach for Connected Robots

Advanced robotic systems are finally becoming a reality; following the increased attention that robotics have attracted during the last few decades, new types of robotic applications are launched, from robotic space vessels and fully autonomous cars to robotic dancers and robot companions. Even more, following the advancements in the Internet of Things (IoT) domain, robots can now participate in more complex systems, namely Cyber-physical systems (CPS). In such systems, robots, software, sensors and/or “things” cooperate seamlessly in order to exhibit the desired outcome. However, the high heterogeneity of the components comprising CPS systems requires expertise in various scientific domains, a fact that makes development of CPS applications a resource- and time-consuming process. In order to alleviate this pain, model-driven (or model-based) approaches have been introduced. They employ a low code software engineering approach and hide the domain-specific knowledge needed, by providing an abstract representation that can be more easily understood. Following the low-code paradigm, current work focuses on the development of Domain-specific Languages (DSL) for ROS2 (Robot Operating System 2) systems in order to hide low-level middleware-specific setup and configuration details and enable access to robot development by non ROS experts. Furthermore, in order to enable the integration of ROS2 robots in CPS, a second DSL was developed. The first language, GeneROS, is used for the development and configuration of the core functionalities of the robot (such as hardware drivers and algorithms), while the second language, ROSbridge-DSL, implements the interfaces for connecting robots to the Edge and the Cloud, enabling this way remote monitoring and control in the context of IoT and CPS.

Detection of Cyberattacks and Anomalies in Cyber-Physical Systems: Approaches, Data Sources, Evaluation

Cyberattacks on cyber-physical systems (CPS) can lead to severe consequences, and therefore it is extremely important to detect them at early stages. However, there are several challenges to be solved in this area; they include an ability of the security system to detect previously unknown attacks. This problem could be solved with the system behaviour analysis methods and unsupervised or semi-supervised machine learning techniques. The efficiency of the attack detection system strongly depends on the datasets used to train the machine learning models. As real-world data from CPS systems are mostly not available due to the security requirements of cyber-physical objects, there are several attempts to create such datasets; however, their completeness and validity are questionable. This paper reviews existing approaches to attack and anomaly detection in CPS, with a particular focus on datasets and evaluation metrics used to assess the efficiency of the proposed solutions. The analysis revealed that only two of the three selected datasets are suitable for solving intrusion detection tasks as soon as they are generated using real test beds; in addition, only one of the selected datasets contains both network and sensor data, making it preferable for intrusion detection. Moreover, there are different approaches to evaluate the efficiency of the machine learning techniques, that require more analysis and research. Thus, in future research, the authors aim to develop an approach to anomaly detection for CPS using the selected datasets and to conduct experiments to select the performance metrics.

Bezbjednost u sajber fizičkim i IoT sistemima

The security of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) is a significant issue due to the increasing integration of these systems into critical infrastructure, production and other industrial environments, as well as the growing use of IoT devices in various areas of life. It is therefore important to consider the security challenges in these systems and work on their resolution in order to maintain the stability and reliability of these systems. This paper discusses the security of CPS and IoT systems, including the architecture of such systems, security requirements and standards, as well as specific security issues that arise in both types of systems. In particular, CPS security is considered through the physical interface, control system, availability and time constraints, while IoT security is considered through trust management, secure routing protocols, integration of heterogeneous networks and privacy protection.

Securing Wireless Communication in Cyber-Physical Systems and the Internet of Things: Addressing Security Challenges

Safeguarding wireless communication in cyber-physical systems (CPS) and the Internet of Things (IoT) is important to stop hackers, data breaches, and system interruptions. Multiple security risks can affect these systems because they are linked together and depend on wireless communication methods. This essay looks at the security issues that mobile and internet-connected things (IoT) have and suggests ways to fix them. Two of the biggest problems are keeping data sent over wifi networks private and secure. You can keep data safe from people listening in or changing it by using encryption methods like Advanced Encryption Standard (AES) and Rivest Cipher (RC4). Furthermore, security tools like Public Key Infrastructure (PKI) and Digital Signatures can confirm the authenticity of devices and make sure that contact is safe. An additional problem is that radio communication methods can be attacked with tactics like man-in-the-middle and repeat attacks. You can make CPS and IoT systems safer by using secure methods like Transport Layer Security (TLS) and Secure Shell (SSH). Protecting radio contact is also important to avoid problems caused by denial-of-service (DoS) attacks. Employing intrusion detection systems (IDS) and firewalls can assist in finding and blocking harmful traffic, ensuring that communication stays unbroken. Lastly, protecting radio transmission in CPS and IoT is important to avoid many security risks. The security of CPS and IoT systems can be improved by using encryption methods, identification mechanisms, and safe protocols to keep data private, secure, and accessible.

Data-Correlation-Aware Unsupervised Deep-Learning Model for Anomaly Detection in Cyber–Physical Systems

A cyber–physical system (CPS) is a multidimensional complex system integrating computing, communication, and control technologies. Because of their key functionality within the system, CPS requires large robustness and security to ensure its reliable operation. Due to its importance in supporting overall system security, anomaly detection (AD) is likely to continue to play an important role in the CPS security. Moreover, unsupervised AD models based on deep learning have shown better performances in rule training, adaptive update, detection efficiency, and accuracy. Due to the nature of the CPS systems, CPS data is more likely to exhibit implicit correlative relationship among data, which would be vital to exploit for CPS security provisions in more complex data environments. In view of this observation, we propose the data-correlation-aware unsupervised deep learning model for AD in CPS, which uses an undigraph structure to store samples and implicit correlation among samples. We design a dual-autoencoder to train both original features and implicit correlation features among data, and we construct an estimation network using Gaussian mixture model (GMM) to evaluate the probability distribution of samples to complete the anomaly analysis. Experimental results compared the performance with relevant AD models based on deep learning which did not use data-correlation analysis. The results showed that, under some representative application scenarios of CPS considered, data-correlation-aware unsupervised deep-learning model achieved superior results in parameter sensitivity, ablation, relationship between correlation degree and detection performance, visualization, and detection effects.

An Evaluation of Cyber Physical System (CPS) or Intelligent System in Which a Mechanism is Controlled or Monitored by Computer Based Algorithms

This article is based on the CPS or cyber-physical system or intelligent system. The term “physical” is associated with natural and also human man-made systems that have been governed by the laws that are connected with physics while operating in continuity. CPS is described as the network of some embedded systems which establishes an interaction with both physical inputs as well as output. CPS faces the challenges of cyber and physical threats as these systems have been found to be of heterogeneous nature that has a reliance on both private and sensitive data. This study has focused on the CPS and CPS components as well. This is the integration of sensing, controlling, networking and computation into some physical objects as well as infrastructures that connect them through the help of the internet with each other. There are some CPS components that have been utilized for sensing different types of information where the CPS components can be classified into Sensing Components (SC) and also Controlling Components (CC). this study also shed light on the “Programmable Logic Controllers” (PLC) have been created for replacing hard-wired relays and have been assumed in the form of “industrial digital computers” controlling manufacturing processes including the performance of robotic performance. This study has also shed light on the improvements in unsupervised intrusion detection within CPS. Intrusion Detection Systems (IDSs) have been becoming certain building blocks for developing the CPS while detecting its potential threats and also triggering the modules to block and also mitigate those adverse impacts from cyber threats. Development via digital twin through CPS. The CPS paradigm has been involved with physical and software components that have been deeply intertwined with each other where each component has been operating on both spatial and temporal scales possessing multiple behavioural modalities and also interacting with each other. Keywords : CPS systems, intelligent algorithms running, intelligent interfaces, open architecture system CPS, RFID- based position detection.

(Retracted) Secure framework against cyber attacks on cyber-physical robotic systems

Robot-based platforms and processes have integrated the security and efficiency of data into a comprehensive range for domains, such as manufacturing, industrial, logistical, agricultural, healthcare, and internet services. Smart cyberattacks have been on the rise, specifically targeting corporate, industrial robotic systems. These attacks execute once the internet of things, internet, and organization integration is implemented with the industrial units. We implemented security criteria-based indices for cyber-physical systems (CPS) with industrial components and embedded sensors that process the information logs and processes. We proposed an attack tree-based secure framework that does not include every CPS device but takes into consideration the critical exploitable vulnerabilities to execute the attacks. We categorized each physical device and integrated sensors based on logs and information in a sensor indices device library. This research simulated real-time exploitation of vulnerabilities on CPS robotic systems using the proposed framework in form of a two-phased process. This validates the enhanced data security output of the integrated sensor and physical nodes with the intelligent monitor and controller system health monitor during real-time cyberattacks This research simulated common cyberattacks on cyber-physical controller servers based on cross-site scripting and telnet pivoting. The authors gathered known and unknown vulnerabilities and exploited them with a tree-based attack algorithm. The authors calculated the average time for cyberattackers with different skills when trying to compromise CPS devices and systems.

Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey

With the booming of cyber attacks and cyber criminals against cyber-physical systems (CPSs), detecting these attacks remains challenging. It might be the worst of times, but it might be the best of times because of opportunities brought by machine learning (ML), in particular deep learning (DL). In general, DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data. DL models are adopted quickly to cyber attacks against CPS systems. In this survey, a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context. A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems. The methodology includes CPS scenario analysis, cyber attack identification, ML problem formulation, DL model customization, data acquisition for training, and performance evaluation. The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules. Moreover, excellent performance is achieved partly because of several high-quality datasets that are readily available for public use. Furthermore, challenges, opportunities, and research trends are pointed out for future research.

A security reference architecture for cargo ports

Secure systems must be built in a systematic and holistic way, where security is an integral part of the development lifecycle and cuts across all architectural layers. This need is more evident in Cyber Physical Systems (CPSs), where attacks may target not only the information model of the system but also its physical entities. CPS systems are heterogeneous and often highly complex. Their possibly numerous components and cross-domain complexity make attacks easy to propagate and security difficult to implement. Moreover, this complexity results in a considerable variety of vulnerabilities and a large attack surface. To design secure CPS systems a good approach is to abstract their complexity and develop a common framework, namely a Reference Architecture (RA), to which we add security mechanisms in appropriate places to stop its threats to define a Security Reference Architecture (SRA). An SRA is an abstract architecture describing a conceptual model of security that provides a way to specify security requirements for a wide range of derived concrete architectures. An important type of CPS is a maritime container terminal, a facility where cargo containers are transported between ships and land vehicles for onward transportation, and vice versa. We present here an SRA for cargo ports built out of patterns, which goes beyond existing models in providing a global view and a more precise description than just block diagrams. Starting from an RA, we analyze security issues in each activity of the processes of the system and enumerate its threats. We describe threats using misuse patterns, and from them we select security patterns that realize defensive solutions.

Comparing cyber physical systems with RFID applications: common attacks and countermeasure challenges

The RFID technology is widely used in industrial control systems (ICS) and cyber physical systems (CPS). However, the design of the current RFID protocol is optimal in performance but with less effort invested into security. As such, RIFD infrastructure could be vulnerable against cyber threats. In this paper, we present a positioning of RFID security attacks with CPS attacks. We identified similarities and differences of both type of threats that can serve as baseline to establish an IoT security framework. Additionally, we discuss RFID security countermeasures that we extracted from the literature then we compare it with existing CPS countermeasures. Three security threats categories were identified namely, physical attacks, interception attacks and blocking attacks. Overall, the research avail that attacks are similar for both CPS and RFID systems apart from social engineering attacks that are specific to CPS. However, addressing those attacks requires additional measures due to RFID specificity.

A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications.

We are still in the midst of Industry 4.0 (I4.0), with more manufacturing lines being labeled as smart thanks to the integration of advanced ICT in Cyber–Physical Systems (CPS). While I4.0 aims to provision cognitive CPS systems, the nascent Industry 5.0 (I5.0) era goes a step beyond, aiming to build cross-border, sustainable, and circular value chains benefiting society as a whole. An enabler of this vision is the integration of data and AI in the industrial decision-making process, which does not exhibit yet a coordination between the Operation and Information Technology domains (OT/IT). This work proposes an architectural approach and an accompanying software prototype addressing the OT/IT convergence problem. The approach is based on a two-layered middleware solution, where each layer aims to better serve the specific differentiated requirements of the OT and IT layers. The proposal is validated in a real testbed, employing actual machine data, showing the capacity of the components to gracefully scale and serve increasing data volumes.

Design and Implementation of Novel BRISI Lightweight Cipher for Resource Constrained Devices

The Internet of Things (IoT) and cyber-physical systems (CPS) has grown exponentially over the recent years, has motivated the development and deployment of the low resource devices for a wide range of applications in the IoT. Many such resource constrained devices are deployed to match the heterogeneous application requirements of IoT and CPS systems, wherein privacy and security have emerged, as the most difficult challenges, as the constrained devices are not been designed to have security features. This paper presents a lightweight cipher, based on ARX (Addition-Modulo, Rotation and XOR) operations, Fiestel structure, an amalgamation of BRIGHT and SIMON structure, hence the name BRISI. The cipher encrypts 32-bit plaintext using 64-bit key. The software implementation is performed using MATLAB tool and it fulfils the Avalanche criterion, Key-sensitivity, correlation coefficient, entropy and histogram. The proposed design is simulated using Xilinx Vivado and is implemented on Nexys-4 DDR Artix-7 and Basys-3 Artix-7 FPGA family and is evaluated for (LUT and register) power and timing

A Security Scheme Based on Intranal-Adding Links for Integrated Industrial Cyber-Physical Systems.

With the advent of the Internet of Everything era, the Industrial Internet is increasingly showing mutual integration and development. Its core framework, the industrial CPS (Cyber-Physical Systems), has received more and more attention and in-depth research in recent years. These complex industrial CPS systems are usually composed of multiple interdependent sub-networks (such as physical networks and control networks, etc.). Minor faults or failure behaviors between sub-networks may cause serious cascading failure effects of the entire system. In this paper, we will propose a security scheme based on intranal-adding links in the face of the integrated and converged industrial CPS system environment. Firstly, by calculating the size of the largest connected component in the entire system, we can compare and analyze industrial CPS systems’ security performance under random attacks. Secondly, we compare and analyze the risk of cascading failure between integrated industrial CPS systems under different intranal-adding link strategies. Finally, the simulation results verify the system security strategy’s effectiveness under different strategies and show a relatively better exchange strategy to enhance the system’s security. In addition, this paper’s research work can help us design how to further optimize the interdependent industrial CPS system’s topology to cope with the integrated and converged industrial CPS system environment.