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The increasing integration of cyber-physical systems (CPS) in critical infrastructures has heightened the importance of ensuring their security against various cyber threats. Game theory has emerged as a powerful analytical tool to model and analyze the strategic interactions between attackers and defenders in CPS. This survey provides a comprehensive review of the state-of-the-art research on the application of game theory in the security of CPS, with a specific focus on attack models and security analysis. We employ machine learning algorithms to classify existing research papers based on the attack target and the types of attacks they address. Our classification reveals significant trends and gaps in the current literature, offering insights into the effectiveness of different game-theoretic approaches and the prevalence of various attack models. By synthesizing the findings from over 800 research papers, we highlight the strengths and limitations of existing methodologies and propose directions for future research. This survey aims to serve as a valuable resource for researchers and practitioners seeking to enhance the security of CPS through game-theoretic frameworks and machine learning techniques.

The increasing focus on cyber-physical security in Smart Grids (SGs) has catalyzed a surge in research over recent years. This paper comprehensively reviews SG cyber-physical security advancements, diverging from conventional studies that concentrate on specific attack types. It begins with a structured overview of SGs, delineating their cyber and physical layers and analyzing the key processes: generation, transmission, distribution, and consumption. Subsequent sections critique existing survey studies, identifying gaps and underscoring overlooked aspects in the current literature, particularly concerning the challenges faced. The review progresses to analyze current research trends in SG security, evaluating methodologies across both layers and categorizing them into Machine Learning-based, data-driven, and model-based approaches. The analysis includes a detailed classification of research focused on Control, Monitoring, and Protection across each component and stage of SGs. Additionally, the paper examines emerging cyberattack strategies in SGs that have not been extensively reviewed in existing literature. In conclusion, the paper reflects on significant gaps and challenges in SG cyber-physical security research, underscoring the need for further exploration and innovation in this domain. Thus, this review serves as a critical roadmap for future research, delineating the current state and potential directions in the rapidly evolving field of SG security.

A crucial element of Industry 4.0, is the utilization of smart devices that generate log files. Log files are key components containing data on system operations, faults (unexpected glitches or malfunctions), errors (mistakes or incorrect actions), and failures (complete breakdowns or non-functionality). This paper presents a systematic mapping study analyzing research conducted on log files for smart-troubleshooting in Industry 4.0. To the best of our knowledge, this is the study that aims to identify research trends, log file attributes, techniques, and challenges involved in log file analysis for smart-troubleshooting. From an initial set of 941 potentially relevant peer-reviewed publications, 74 primary studies were selected and analyzed using a meticulous data extraction, analysis, and synthesis process. The results of the study demonstrate that the majority of research has focused on developing algorithms for log analysis, with machine learning being the most commonly used approach. The smart-troubleshooting encompasses a range of activities and tools that are essential for collecting failure data generated by diverse interconnected devices, conducting analyses, and aligning them with troubleshooting instructions and software remedies. Moreover, the study identifies the need for further research in the areas of real-time log analysis, anomaly detection, and the integration of log analysis with other Industry 4.0 technologies. In conclusion, our study provides insights into the current state of research in log analysis for smart-troubleshooting in Industry 4.0 and identifies areas for future research. The use of smart devices generating log files in Industry 4.0 highlights the importance of log file analysis for troubleshooting purposes. Further research is needed to address the challenges and opportunities in this field to integrate log analysis with other Industry 4.0 technologies for performing more efficient and effective troubleshooting.

Internet of Battlefield Things (IoBT) is one of the latest technological advancements in combat-aid services, designed to enhance the battlefield operations of elite teams, such as seals and special operation units. Based on the general concept of the Internet of Things, IoBT establishes a set of devices connected via a communication channel that can interact with each other and share data easily over the battlefield area. In contexts characterized by a high level of difficulties in operation, location information of soldiers should be kept at accuracies that are the highest. This need forms the basis for location privacy, where protection of the positional information of soldiers is ‘deception’, and on which this academic paper is based. Within the ambit of deception strategies for IoT, this article delves into the nuanced intricacies of one particularly promising approach, ‘mix-zones’, which we previously proposed by us for IoBT environments. These zones facilitate the amalgamation and anonymization of multiple user locations within expansible spatial boundaries, thereby offering a robust mechanism for preserving location privacy amidst dynamic battlefield environments. This article is the extension of our previously published explanation work “Expandable Mix-Zones for the IoBT”. For this version, we extend the proposed scheme by including a Random Walk model, hence strengthening the theoretical basis with empirical analysis. More specifically, in this work, we try to estimate the approximate precision of the position information inside and outside mix-zone boundaries. By incorporating the Random Walk model, we try to explain the dynamic interaction between location privacy protection and the inherent uncertainty of the battlefield. The simulations are done in the Python environment, and the associated graphics for each are provided. This approach allows one not only to ensure reproducibility but also to enhance readers’ involvement with the provision of full-scale visual exploration of comparative studies. Concretely, this work investigates the efficacy of the mix-zone concept in real-world scenarios to derive practical insights that are essential for creating robust and resilient IoBT infrastructures.

Advancements in technology are propelling Cyber-Physical Systems (CPS) into crucial roles across various sectors, implying the need for stricter CPS safety and security measures as their deployment in safety-critical scenarios increases. Physical-to-cyber attacks are particularly alarming among emerging threats, targeting sensors and exposing significant vulnerabilities in CPS due to the inadequacy of current protection mechanisms. The development landscape for CPS also reveals other gaps such as the insufficiency of analog fault coverage and validation, and the threat of covert malicious circuit alterations by third-party outsourcing. This paper seeks to draw the scientific community’s attention to these topics from a unified perspective, presenting the main issues in the development of safety-critical CPS, along with a survey of related subjects. Subsequently, these topics are formally bridged with the introduction of this paper’s main contribution: the concept of Hardware Integrity Threats (HITs). This finding suggests that the community can develop countermeasures that are effective across the spectrum of these threats, which led to the formulation of two environment proposals to explore these design spaces. Our proposals incorporate co-simulation and simulation with Hardware-in-the-Loop (HiL) taking these safety and security validation concerns into account, as the exploration of these designs mostly entails hybrid analog hardware/software solutions.

Modern power grids and other complex systems are a fusion of physical and cyber components, giving rise to intricate interdependencies. These interdependencies, however, also expose vulnerabilities that can be exploited by adversaries. This paper delves into the critical examination of these interconnections, inspired by Ecological Network Analysis (ENA). By drawing from ecological modeling, we aim to understand the role of cyber-physical interdependencies in the resilience of complex systems. We introduce various modeling methods, including bipartite and tripartite networks, to analyze and map these interdependencies in the context of the IEEE WSCC 9-bus and the ACTIV 200-bus case study. The paper explores how these models can identify key actors and assess network resilience. Through a detailed methodology, we apply ecological metrics and community identification techniques to comprehensively evaluate the system’s interactions. The findings offer insights into the interplay of cyber and physical elements in power grids and other complex systems. These analysis methods show that tripartite networks produce more information on indirect interactions within a complex network. Additionally, they provide detailed information on how disturbances could propagate in a cyber-physical power system. Denial of service scenarios for the WSCC 9-bus and the ACTIV 200-bus case studies are employed to support this conclusion.

The continuing proliferation of Cyber Physical Systems (CPSs), those that integrate electronic control circuitry with a mechanical system allowing software commands to affect the physical world, while allowing for new efficiencies and convenience, also facilitates new security risks. While the cyber-domain components of a CPS control the behavior of the physical-domain components, attacks on either the physical-domain or cyber-domain can be used to control or subvert the CPS in ways that may cause serious harm or even loss of life. Unfortunately, most research in CPS attack detection has focused solely on monitoring the cyber domain for malicious software activity, and the few works that do monitor the physical domain neglect or independently monitor the cyber domain. With that in mind, this paper proposes a method of jointly monitoring both domains (cyber and physical) of a CPS, verifying not only that software commands are faithfully executed by the cyber-components of the CPS, but also that the physical-domain behavior of the CPS corresponds to those commands. We demonstrate the effectiveness of this approach through experiments using a medical CPS device subjected to both cyber and physical attacks. We show that different analog side channels (EM and acoustic in our experiments) are similarly effective for such monitoring and attack detection. We also show that, while some attacks can be detected through physical-domain monitoring alone or through cyber-domain monitoring alone, our joint monitoring of both domains allows for the detection of additional attacks that cannot be detected by monitoring only one of the domains, resulting in >99% attack detection accuracy measured over 1,000 instances of CPS activity.

Cybersecurity challenges pose a significant threat to Healthcare Cyber Physical Systems (CPS) because they heavily rely on wireless communication. Particularly, jamming attacks can severely disrupt the integrity of these CPS networks. This research introduces a decentralized system to address this issue. Therefore, this paper suggested a system that leverages trust and blockchain technology to detect jamming attacks in healthcare CPS effectively. It proposes a layered model to improve CPS networks’ lifetime and performance. In smart healthcare environments, it ensures secure and reliable communication between sensor nodes, wearable sensors, medical devices, and monitoring systems. Results show that the suggested approach outperforms the baseline model in identifying and minimizing jamming assaults, with an average percentage difference of 15.71% more detection rate, 20.21% less packet loss rates, 16.65% less node-level energy consumption, reduced network latency of 8.29%, and 9.63% more network throughput.

Nowadays, cyber-physical systems (CPSs) have gained remarkable attention in electrical and control engineering. However, their vulnerability to attacks poses challenges for control. This report explores the adaptive dynamic surface control approach for CPSs against both injection and deception attacks on states and the control signal with unknown direction, suggesting using Nussbaum functions. Given the comprehensive nature of nonlinear time-delay CPSs, this research focuses on designing an adaptive resilient controller for such systems. To address the challenges of time-delay CPSs without restrictive assumptions, specific even functions are applied to resolve signal zeroing in the controller’s denominator due to time-varying delays. Utilizing these functions in dynamic surface control, presents challenges that are addressed in this study. The proposed method ensures the boundedness of all closed-loop signals and two simulation examples are given to show how well it works.

Cyber-physical systems (CPS) are recognized for their intelligence as they seamlessly interact with humans, enhancing the physical world through computation, communication, and control. Over the last few years, the evolution of CPS, including IoT components, has significantly impacted many facets of people’s lifestyles. It has been immersed in a wide range of services and applications in various areas, including manufacturing, healthcare, and energy. However, the interrelationship between the cyber and physical worlds gives rise to a multitude of research problems and challenges. In the healthcare field, for instance, CPS introduces complexities related to interoperability, privacy, data security, and real-time data processing with critical implications for the reliability and safety of medical processes. To address these challenges and harness the full potential of CPS in healthcare, this paper, through a comprehensive literature review, aims to discuss cutting-edge CPS technologies and solutions that hold promise for healthcare applications. To this end, we propose a comprehensive architectural model that can serve as a benchmark for implementing CPS in healthcare applications. This model thoroughly details how services, components, and technologies can be integrated to transform massive raw data collected from the physical world into valuable information for an enhanced decision-making process. Finally, a use case on healthcare CPS is presented, outlining its characteristics, the role that different technologies have played in its development, and the major challenges in implementing such systems successfully. This study provides a cohesive understanding of the role CPS can play in empowering healthcare while offering insights into the challenges, and future research trends.

This paper describes a simulation-based approach for automated risk assessment of complex cyber-physical systems to support implementers of ISO 27005. The approach is based on systematic cause-and-effect modelling of threats, their causes and effects, and the ways in which the effects of one threat can lead to other threats. In this way, the approach deals with inter-dependencies within the target system, automatically finding attack paths and secondary effect cascades, which generally are very complex and the source of many challenges when implementing ISO 27005. The approach uses a knowledgebase describing classes of system assets and their possible relationships, along with the associated threats, causes and effects in a generic context. A target system can then be modelled in terms of related assets, describing the intended system structure and purpose (in the absence of any deviations). The knowledgebase is then used to identify which threats are relevant and create a cause-and-effect simulation of those threats. This allows threat likelihoods and risk levels to be found based on input concerning trust assumptions and the presence of controls in the system. The approach has been implemented by the open source Spyderisk project and validated by modelling a published case study of an attack on a steel mill. Given reasonable assumptions about security controls in place, the shortest, highest likelihood attack path found coincides with the published analysis. The case study demonstrates the strengths of the approach: transparency, reproducibility, and performance.

In this manuscript, we introduce a model predictive control (MPC) strategy tailored to improve the security and resilience of constrained cyber-physical systems facing attacks whose target is the communication channel. Building upon a recently developed resilient control framework, the improvement consists in addressing the challenges posed by time length varying denial-of-service attacks. Its primary focus lies in its capacity to mitigate undesired system behaviors stemming from external malicious actions.Central to our strategy consists in using set-membership conditions, facilitating swift detection of data integrity anomalies and enabling the implementation of appropriate countermeasures. By harnessing set-theoretic based model predictive control ideas, the resulting solution emphasizes its resilient nature in the efficient handling of explicit plant constraints. Moreover, the proposed scheme is designed to ensure the system’s safe operation during attacks and minimizing performance degradation. To further bolster resilience, a network refresh procedure is added, which serves to securely restore attack-free communications should the initially deployed countermeasures prove insufficient. Through extensive simulations, wherein we apply our methodology to a tracking problem involving a planar mass-point system, the effectiveness of the resilient scheme here proposed is proved. These simulations confirm the capability of our control architecture to maintain constraint satisfaction across a spectrum of possible denial of service attack scenarios, thereby underlining its robustness and practical usefulness in safeguarding cyber-physical systems against malicious threats.

The transformation to Industry 4.0 also transforms the processes of developing intelligent manufacturing production systems. Digital twins may be employed to advance the development of these new (embedded) software systems. However, there is no consensual definition of what a digital twin is. In this paper, we provide an overview of the current state of the digital twin concept and formalize the digital twin concept using the Object-Z notation. This formalization includes the concepts of physical twins, digital models, digital templates, digital threads, digital shadows, digital twins, and digital twin prototypes. The relationships between all these concepts are visualized as class diagrams using the Unified Modeling Language. Our digital twin prototype approach supports engineers in the development and automated testing of complex embedded software systems. This approach enables engineers to test embedded software systems in a virtual context without the need of a connection to a physical object. In continuous integration/continuous deployment pipelines, such digital twin prototypes can be used for automated integration testing and, thus, allow for an agile verification and validation process. In this paper, we demonstrate and report on the application and implementation of a digital twin using the example of two real-world field studies (ocean observation systems and smart farming). For independent replication and extension of our approach by other researchers, we provide a laboratory study published open source on GitHub.

This study proposes an innovative approach specifically devised for discriminating faults and replay attacks in the context of hybrid system, significantly advancing cyber-physical system security. At the core of our method is the intricate process of embedding distinct series of non-recurring alphanumeric codes in each cell of the output matrix. This advanced encoding strategy is essential for the accurate identification of replay attacks, as it allows for the detailed analysis of code pattern variations that are indicative of malicious activities. Our approach is uniquely effective in revealing subtle anomalies and replicated data patterns, marking a substantial enhancement in the detection of sophisticated cyber threats. Complementing this is the implementation of a Sliding Mode Observer for precise fault detection, enabling our system to simultaneously differentiate between faults and replay attacks. This dual detection strategy is thoroughly demonstrated in complex system scenarios, highlighting its effectiveness as a robust defense mechanism. The introduction of this innovative method represents a significant step forward in fortifying the security and integrity of modern hybrid systems. A four-tank hybrid system is considered as a case study, to show the effectiveness of the proposed approach in identifying and countering faults and replay attacks within the system. Notably, the error rate of exposing the replay attack is remarkably low, achieving an accuracy of 99.8%.

Abstraction-based formal synthesis with a symbolic control barrier function is useful for obtaining a finite-state safe controller for an infinite system with sporadic disturbances. In the case of multiple mobile robots sharing a common workspace, a controller ensuring arrival to destinations without any collisions is obtained with the symbolic control barrier function, despite the existence of unpredictable sporadic packet dropouts among robots. In the existing method, a local abstracted model of each robot is constructed, they are composed to obtain an abstracted model of the entire system, and unsafe transitions in it are eliminated with a symbolic control barrier function. However, a considerable computation time is required when the number of robots is large. To solve this problem, a shield called an alternating simulation-based shield (AS-Shield) was introduced into abstraction-based formal synthesis. As well as the existing method, a local abstracted model for each robot was constructed. Instead of the composition, a local controller was constructed for each robot, and a safe controller for the robot was obtained by attaching an AS-Shield. Because a composition to obtain the entire system is not necessary, the control inputs are computable, even though the number of robots is large. To confirm the validity of the proposed method, it was implemented using a robot simulator.

The underwater environment is among Earth’s most challenging domains, where failures incur elevated risks and costs for both technology and human endeavors. As a consequence, forecasting the behavior of mechatronic systems through simulation has become increasingly important. Underwater Robotic Simulators (URSs) allow researchers and engineers to safely develop and assess submarine systems. The selection of an appropriate URS from the list of available tools is not trivial. Moreover, the integration of this software with the Digital Twin (DT) concept presents numerous advantages, particularly the ability to link the simulated environment with actual underwater vehicles. This connection is facilitated by performing validation and simulation tests using Software In the Loop (SIL), Model In the Loop, and Hardware In the Loop (HIL) techniques. This paper extensively reviews URSs in the context of both robots and unmanned vehicles in light of the DT paradigm. The article critically examines distinctions among existing URSs, offering valuable insights to aid researchers in selecting the most fitting tool for their specific applications. Additionally, the review explores the practical applications of the identified simulators, categorizing their usage across different fields to illuminate the preferences within the scientific community and showcase prominent case studies.

The demand for cyber-physical systems (CPSs) has recently increased in various domains, such as smart grids, intelligent transportation, and critical infrastructure. The massive data networks and communication layers generated make CPSs vulnerable to threats and cyberattacks. To mitigate these threats, artificial intelligence (AI) approaches are employed. However, AI models struggle to keep up with the constantly changing attack landscape. This study investigates the application of extreme gradient boosting (XGBoost) and long-short-term memory (LSTM) AI models for cyberattack detection in a CPS. Accuracy, precision, recall, and the F1-score validate the approach as evaluation metrics. The methods were tested on a gas pipeline industrial control system dataset and other benchmark datasets, such as NetML-2020 and IoT-23, which contain various cyberattacks. The performance of the two methods was found to be better than other models such as support vector machine (SVM) and artificial neural networks (ANN) on several evaluation metrics. Finally, we present recommendations for future research.

With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible “Co-LivEn” dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.

Inspired by the cyber-physical systems (CPS) of numerical methods for stochastic differential equations, we present a CPS model of sampled-data control systems (typically a synonym for computer control systems), which regards the intersection of the physical and the cyber (the key feature of CPS). As a theoretic foundation, we develop by the Lyapunov method a stability theory for a general class of stochastic impulsive differential equations (SiDE) which is formulated as a canonical form for CPS that may work in feedback loops and thus include those of sampled-data control systems. Applying the fundamental theory, we study stability of the CPS, which implies that of the sampled-data control system. By our CPS approach, we not only obtain stability criteria for the CPS of sampled-data control systems but also reveal the equivalence and intrinsic relationship between the two main approaches (viz. controller emulation and discrete-time approximation) in the literature. As the applications of our CPS theory, we propose a control design method for feedback stabilization of the CPS of sampled-data stochastic systems. Illustrative examples are conducted to verify that our method significantly improves the existing results. In this paper, we initiate the study of a systems science of design for CPS. This provokes many open and interesting problems.

Smart grid technology drives economic and social development but raises vulnerability to cyber threats due to digital substations’ growing reliance. To counter these risks, recent updates to communication standards, including encryption and authentication processes, have been integrated into the infrastructure. Notably, adhering to the IEC 62351 standard governing the GOOSE protocol for substation communication faces practical challenges due to conflicting time requirements with traditional security procedures. In this paper, we present an innovative geometric approach for GOOSE message authentication and encryption. Utilizing vector coordinate shifts, our method exhibits efficiency and speed of implementation, ensuring compliance with the protocol’s stringent time constraints. Importantly, unlike other approaches in the literature, our technique has the potential to be easily applicable and effective for a wide range of infrastructures without requiring the use or addition of specific hardware components or changes to the GOOSE message format, while guaranteeing high performances and computational simplicity. The paper is complemented by a simulation campaign on a digital substation model, assessing the approach’s performance and its efficacy in countering cyber threats.

The security of Cyber-Physical Systems (CPSs) is increasingly important as more and more of these systems are added to the Internet of Things (IoT). As we increase the complexity and connectivity of our smart systems, we likewise broaden their digital attack surface. Recorded attacks on CPSs have caused significant physical impacts making methods for mitigation of attacks of paramount importance. The use of runtime enforcement (RE) can prevent violation of security policies. Here, runtime enforcers intervene before the CPS is compromised. Two key challenges are presented: (1) for complex systems, methods for automatically composing multiple policies are lacking; and (2) runtime enforcers are themselves executed digitally—meaning they too could have potential security vulnerabilities. We present the first comprehensive runtime enforcement framework which addresses both challenges. It can compose a lot of security policies in parallel and synthesize these policies into the more trustworthy hardware layers of a system. This removes reliance on potentially vulnerable firmware and software layers. We demonstrate our approach with policies to mitigate a set of attacks on a Fused Filament Fabrication (FFF) 3D printer. The experimental results show linear growth in logic element and register usage as the number of policies increase. This compares favourably to the exponential state space explosion that occurs with the conventional approach of monolithic composition. Additionally, we find higher enforcer clock frequencies are possible with the proposed parallel approach compared to existing serial approaches.

The demand for more developed and agile urban taxi drones is increasing rapidly nowadays to sustain crowded cities and their traffic issues. The critical factor for spreading such technology could be related to the safety criteria that must be considered. One of the most critical safety aspects for such vertical and/or Short Take-Off and Landing (V/STOL) drones is related to safety during the landing stage, in which most of the recent flight accidents have occurred. This paper focused on solving this issue by proposing decentralized processing cores that could improve the landing failure rate by depending on a Fuzzy Logic System (FLS) and additional Digital Signal Processing (DSP) elements. Also, the proposed system will enhance the safety factor during the landing stages by adding a self-awareness feature in case a certain sensor malfunction occurs using the proposed Adaptive Prognostic Malfunction Unit (APMU). This proposed coarse-grained Autonomous Landing Guidance Assistance System (ALGAS4) processing architecture has been optimized using different optimization techniques. The ALGAS4 architecture has been designed completely using VHDL, and the targeted FPGA was the INTEL Cyclone V 5CGXFC9D6F27C7 chip. According to the synthesis findings of the INTEL Quartus Prime software, the maximum working frequency of the ALGAS4 system is 278.24 MHz. In addition, the proposed ALGAS4 system could maintain a maximum computing performance of approximately 74.85 GOPS while using just 166.56 mW for dynamic and I/O power dissipation.

This paper reports on the use of co-simulation techniques to build prototypes of co-operative autonomous robotic cyber-physical systems. Designing such systems involves a mission-specific planner algorithm, a control algorithm to drive an agent performing its task; and the plant model to simulate the agent dynamics. An application aimed at positioning a swarm of unmanned aerial vehicles (drones) in a bounded area, exploiting a Voronoi tessellation algorithm developed in this work, is taken as a case study. The paper shows how co-simulation allows testing the complex system at the design phase using models created with different languages and tools. The paper then reports on how the adopted co-simulation platform enables control parameters calibration, by exploiting design space exploration technology. The INTO-CPS co-simulation platform, compliant with the Functional Mock-up Interface standard to exchange dynamic simulation models using various languages, was used in this work. The different software modules were written in Modelica, C, and Python. In particular, the latter was used to implement an original variant of the Voronoi algorithm to tesselate a convex polygonal region, by means of dummy points added at appropriate positions outside the bounding polygon. A key contribution of this case study is that it demonstrates how an accurate simulation of a cooperative drone swarm requires modeling the physical plant together with the high-level coordination algorithm. The coupling of co-simulation and design space exploration has been demonstrated to support control parameter calibration to optimize energy consumption and convergence time to the target positions of the drone swarm. From a practical point of view, this makes it possible to test the ability of the swarm to self-deploy in space in order to achieve optimal detection coverage and allow unmanned aerial vehicles in a swarm to coordinate with each other.

The lifespan of overhead transmission conductors is influenced by various factors, including design, maintenance, and operating conditions. Prolonged exposure to high temperatures, particularly near maximum design values, can significantly age transmission lines (TLs). The dynamic thermal rating (DTR) system, an advanced technology that increases TL capacities, can contribute to these stringent conditions. DTR overlays a cyber layer with communication and control systems onto the physical network, transforming it into a cyber-physical network. Currently, the increased risk of TL ageing associated with DTR, and the cyber layer’s functionality lack a unified framework for quantification. This study introduces a novel framework that considers the cyber layer’s network topology and its impact on TL ageing due to DTR implementation. It also proposes the implementation of system integrity protection schemes (SIPS) within the framework. SIPS detects abnormal conditions from DTR deployment, like excessive line loading, and takes corrective actions to optimize DTR system’s performance. The study employs a Sequential Monte Carlo Simulation (SMCS) technique on a modified cyber-physical IEEE RTS-79. The study reveals that topologies like double ring and mesh, cause higher line ageing indices compared to line and star topologies. ETNA values remain relatively stable across the network topologies within a year, suggesting minimal short-term impact on transmission line ageing. However, cumulative effects over several years can potentially reduce lifespan. SIPS can slow network ageing by up to 5hr/yr, but their contingencies can cause the opposite effect. Nonetheless, a reliable SIPS can maintain an ETNA value of 101hr/yr without increasing load curtailment.

The integration of wireless antenna sensors for cyber-physical systems has become increasingly prevalent in various biosimilar applications due to the escalating need for monitoring techniques that are efficient, accurate, and reliable. The primary objective of this comprehensive investigation is to offer a scholarly examination of the present advancements, challenges, and potentialities in the realm of wireless antenna sensor technology for monitoring biosimilars. Specifically, the focus will be on the current state of the art in wireless antenna sensor design, manufacturing, and implementation along with the discussion of cyber security trends. The advantages of wireless antenna sensors, including increased sensitivity, real-time data gathering, and remote monitoring, will next be discussed in relation to their use in a variety of biosimilar applications. Furthermore, we will explore the challenges of deploying wireless antenna sensors for biosimilar monitoring, such as power consumption, signal integrity, and biocompatibility concerns. To wrap things off, there will be a discussion about where this subject is headed and why collaborative work is essential to advancing wireless antenna sensor technology and its applications in biosimilar monitoring. Providing an in-depth overview of the present landscape and potential developments, this article aims to be an asset for academics and professionals in the fields of antenna sensors, biosimilar development, wireless communication technologies, and cyber physical systems.

Metal Additive Manufacturing (MAM) produces complex, part geometries from a variety of materials in powder and wire form. Due to complexities of MAM processes that create those geometries, especially powder bed fusion, quality assurance, and qualification remain an ongoing challenge. Quality assurance involves assessing the quality of a part’s geometry, surface finish, and mechanical properties. Currently, quality assurance is not easily achieved due to variations in the powder inputs, the MAM process itself, and environmental factors, such as temperature. Many efforts are underway to develop a new quality system that includes 1) planning, measuring, and qualifying parts and 2) enhancing quality through a processing-monitoring-qualifying framework. Creating this new system requires building the complex relationships between requirements, processes, and quality. These relationships are needed to specify, measure, analyze, and optimize variables to ensure final part quality. Thus, a processing-monitoring-quality framework could provide critical steps to identify those relationships and help meet stakeholder needs. The paper describes how to adapt the “software and systems engineering” V-model to a “metal AM quality assurance” V-model that can provide a framework for quality assurance in MAM.

Cyber-Physical Systems (CPSs) combine computation, networking, and physical processes. A CPS consists of various independent subsystem components with different interfaces and protocols defined by vendors. Utilizing different interfaces and protocols with the necessity for flexible, dependable, and extensible CPSs poses new challenges for controlling, communicating, and synchronizing the components. In this paper, we propose a Centralized Control Unit (CCU) for CPSs and the associated Communication Infrastructure that enable the support of subsystem components as “plug-and-play” modules. Our CCU consists of a hardware-software architecture that handles physical signals as well as real-time and non-real-time tasks implemented on a single MPSoC-FPGA. To enforce the dependability and flexibility of the communication between components of different vendors, we model the Communication Infrastructure in layers with communication classes. In addition, we propose three vendor-agnostic application protocols, which are also implemented in the CCU. To validate our work, we have implemented and integrated the CCU with the communication infrastructure into an open-interface Computed Tomography (CT) scanner, where sensors/actuators can be added in a “plug-and-play” fashion. We have also evaluated the effort of integrating an additional detector into our scanner. The CCU runs on an AMD-Xilinx Zynq-7000 XC7Z045, and it uses only 10% of the hardware resources.

With the increasing demand for human–robot collaboration (HRC), intuitive interfaces are essential to connect humans and robots. A promising approach is the use of mixed reality (MR) to enhance spatial understanding through virtual and augmented reality. In this paper, we propose a novel HRC system that extracts human handling procedures and generates concrete motion plans for the robot. The user, wearing an MR device, interacts with virtual objects in the MR space using natural hand motions. These motions and resulting state transitions are abstracted into a symbolic semi-order motion planner represented by the reachability graph (RG). Using the RG, an autonomous behavior tree is generated, considering the robot’s task environment, and the concrete motion plan is executed by the robot. This system allows the robot to take a more flexible approach to user instructions than conventional MR-HRC systems. Moreover, this system translates human orders into plans that are independent of a specific robot, demonstrating considerable development potential.

Modern cyber-physical systems (CPS) are interdependent, mechanical and IT components that support operations in most of society’s critical infrastructures. Time and again history has proven that CPS are vulnerable to numerous types of threats, ranging from safety accidents to cybersecurity malicious attacks. Current research focuses on analyzing the various input vectors in CPS, whether mechanical or IT, to detect and patch flaws and vulnerabilities to mitigate potential impact in operation. Still, there is little work that can inherently analyze the software-based implementation of modern CPS with complex behavior and failure modes. The only vaguely relevant approach involves an operations-based experimentation methodology from NetFlix named chaos engineering (CE) that tests use cases on complex Content Delivery Networks (CDN) to build confidence in their capability to withstand turbulent conditions in production. Conditions can range from hardware failures to DoS attacks, to a malformed injection appearing in a runtime configuration parameter. Yet this approach was only tested on software based CDN, and not on CPS with industrial actuators and mechanical parts that control physical processes. In this paper, we introduce a novel framework that combines CE with digital twin (DT) technology to enhance the detection of operational vulnerabilities and increase the resilience of CPS. To achieve this objective, we integrated CE experimentation into the simulation phase of DT models using material flow networks. This allows us to assess system resilience during the operational stage without disrupting critical operations and identify vulnerable flows and processes within the modeled system. To evaluate the effectiveness of our approach, we conduct experiments on a DT that models a real-world Liquefied Petroleum Gas purification process from an existing oil refinery in the Mediterranean area. The results of these experiments demonstrate the method’s effectiveness in capturing the heightened susceptibility of the Gas purification process to adverse events.

With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, mechanisms to control/optimize transmission and distribution grid voltage would experience a paradigm shift. Given the introduction of inverter-based DERs with vastly different dynamics, real-world performance characterization of the cyber-physical system (CPS) in terms of dynamical performance, scalability, robustness, and resiliency with the new control algorithms require precise algorithmic classification and suitable metrics. It has been identified that classical controller definitions along with three inter-disciplinary domains, such as (i) power system, (ii) optimization, control, and decision-making, and (iii) networking and cyber-security, would provide a systematic basis for the development of an extended metric for algorithmic performance evaluation; while providing the taxonomy. Furthermore, a majority of these control algorithms operate in multiple time scales, and therefore, algorithmic time decomposition facilitates a new way of performance analysis. Extended discussion on communication requirements while focusing on the architectural subtleties of algorithms is expected to identify the real-world deployment challenges of voltage control/optimization algorithms in the presence of cyber vulnerabilities and associated mitigation mechanisms affecting the controller performance with DERs. Finally, the detailed discussion provided in this paper identifies the modeling requirements of the CPS for real-world deployment, specific to voltage control, facilitating the development of a unified test-bed.

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm.

Smart contracts are software systems that monitor, automate, and control the execution of a process, react to violations, and enforce process terms and conditions. There is tremendous interest in developing smart contract applications in banking, finance, insurance, government, and supply chain markets. Many of these applications operate in a cyber-physical environment and adopt architectures based on Internet-of-Things (IoT) and blockchain technologies to support monitoring and ensure data integrity. To investigate how cyber-physical smart contracts are realized for compliance monitoring, together with associated challenges and research opportunities. A mapping review of the literature that surveys underlying architectures and their evaluation. The publications considered came from four databases (Scopus, Web of Science, IEEE Xplore, and Google Scholar), supplemented by manual snowballing. All publications considered are peer-reviewed, written in English, and published in non-predatory venues. A total of 368 publications were considered, with a final selection of 50 papers (all published between 2018 and 2023) that were analyzed along three dimensions: Cyber-physical architectures, infrastructure failures, and technical challenges. Blockchain technologies are the most commonly used platform for smart contracts as they provide decentralized architectures deploying interesting communication patterns, as well as multiple technologies to simplify communication for producing and consuming events. Moreover, such architectures can lead to many types of infrastructure failures including sensor/actuator attacks, network outages, and hardware/software failures, resulting in five important technical challenge areas related to security, availability, robustness, privacy, and legal aspects. Key insights and directions for future research are also reported. This review will inform readers about how cyber-physical smart contracts are being built and deployed and the challenges that are faced by their builders and users.

The increase in connectivity and the continued need to evolve existing systems impose on system engineers the reality of dealing with System of Systems (SoS) concept in practice. The composition of legacy systems presents significant challenges, especially when a Cyber-Physical System (CPS) is involved. Due to the complexity of the interactions between CPS computational solutions and the physical environment, added to the operational independence of CPS as constituent system, emergent behaviors might unpredictably emerge when the CPS and other system run to collaborate being difficult to control. The purpose of this article is to present the Discovery and Requirement Canvas method and its contributions on highlighting emergent behavior. Introduced in the context of EMBED-SoSE approach, the method supports the process of designing a CPSoS in the early phases of a project, when the stakeholders’ needs, constraints, and alternatives must be analyzed in terms of the integration possibilities of existing constituent systems. An experimental study conducted with 22 senior professionals from the software and aerospace industries validates the practicability of the method Discovery & Requirement Canvas demonstrating its efficacy. The results of the experiment are compared using descriptive and inferential statistics. In addition, the method was applied to a real-world case study in the field of monitoring natural disasters.

Traffic congestion is a major source of discomfort and economic losses in urban environments. Recently, the proliferation of traffic detectors and the advances in algorithms to efficiently process data have enabled taking a data-driven approach to mitigate congestion. In this context, this work proposes a reinforcement learning (RL) based distributed control scheme that exploits cooperation among intersections. Specifically, a RL controller is synthesized, which manipulates traffic signals using information from neighboring intersections in the form of an embedding obtained from a traffic prediction application. Simulation results using SUMO show that the proposed scheme outperforms classical techniques in terms of waiting time and other key performance indices.

This study addresses the issue of secure control design for cyber-physical systems (CPS) against denial of service (DoS) attacks. We take into account a continuous-time linear system with a convex quadratic performance measure and a sampled linear state feedback control. DoS attacks impose constraints on the CPS, where packets may be jammed between the sensor and controller by a malicious entity, potentially resulting in system instability and performance degradation. We assume that the attacker can perform DoS attacks with a limited time and frequency due to energy restrictions. We devise an efficient procedure using the linear matrix inequality approach to compute an upper bound on the performance degradation brought on by the DoS attack. We also propose a redesign of the controller to minimize this performance degradation. Finally, a simulation example illustrates the computation of the performance degradation under a bounded DoS attack and the design of a secure controller. Simulation results show that the designed controller effectively keeps the feedback loop’s performance and stability under attack.

Critical infrastructure (CI), such as energy and water distribution systems, is essential for the stability and well-being of the modern society. Industrial automation and control systems (IACSs) form the backbone of CIs and enable the operation of such systems in a safe and reliable manner. However, with the increasing use of industrial Ethernet communication protocols, such as Modbus-over-TCP (Modbus/TCP), once air-gapped IACSs are becoming vulnerable to potential cybersecurity threats. This paper presents a novel method for enhancing the cybersecurity of Modbus/TCP-based IACSs by implementing an authentication method based on message authentication codes (MACs). To provide partial protection of communication even when communicating with legacy Modbus/TCP peers, we propose a novel supervising device that analyzes exchanged messages and verifies the authenticity of the protected messages. To experimentally verify the protection method, a water-treatment cyber-physical system (CPS) was implemented as a digital twin in a programmable logic controller (PLC). The underlying MAC is the Chaskey-12, lightweight MAC defined in IEC 29192-6. It was implemented in the PLC program using the programming languages defined in IEC 61131-3. As an additional contribution, the presented implementation allows protection of communication between PLCs and other Modbus/TCP peers installed in existing IACSs without hardware or firmware modifications. The results show that the method provides protection against network attacks without significantly affecting performance, also demonstrating the feasibility of such protection in IACSs.

Modern technological industries fused with the Internet-of-Things (IoT) have been advancing rapidly. The joint usage of several technologies has led to the reshaping of the modeling and simulation techniques into the virtualization of physical systems. Thus, the concept of virtual prototyping has emerged as a significant development in distributed IoT applications that includes early exploration, optimization, and security assessments. Several industries have been employing various types of prototyping e.g., virtual platforms, digital twins, and application-specific virtualization techniques, to achieve individual needs for development. In this survey, we clarify some of these concepts and the distinctions between them, provide a comprehensive overview of various prototyping technologies, and discuss how several virtualization technologies play a transformative role in the design and operation of intelligent cyber-physical systems.

In this paper, we present the design, implementation, and evaluation of a secure sensing data processing and logging system. The system is inspired and enabled by the blockchain technology. In this system, a public blockchain is used as immutable datastore to log the most critical data needed to secure the system. Furthermore, several innovative blockchain-inspired mechanisms have been incorporated into the system to provide additional security for the system’s operations. The first priority in securing sensing data processing and logging is admission control, i.e., only legitimate sensing data are accepted for processing and logging. This is achieved via a sensor identification and authentication mechanism. The second priority is to ensure that the logged data remain intact overtime. This is achieved by storing a small amount of data condensed from the raw sensing data on a public blockchain. A Merkel-tree based mechanism is devised to link the raw sensing data stored off-chain to the condensed data placed on public blockchain. This mechanism passes the data immutability property of a public blockchain to the raw sensing data stored off-chain. Third, the raw sensing data stored off-chain are secured with a self-protection mechanism where the raw sensing data are grouped into chained blocks with a moderate amount of proof-of-work. This scheme prevents an adversary from making arbitrary changes to the logged data within a short period of time. Fourth, mechanisms are developed to facilitate the search of the condensed data placed on the public blockchain and the verification of the raw sensing data using the condensed data placed on the public blockchain. The system is implemented in Python except the graphical user interface, which is developed using C#. The functionality and feasibility of the system have been evaluated locally and with two public blockchain systems, one is the IOTA Shimmer test network, and the other is Ethereum.

Cyber-Physical attacks (CP-attacks) are launched either from the cyber-space or from the physical-space to take control of a Cyber-Physical System (CPS). Unlike conventional cyber-attacks, which are prevented through new security patches as new attacks emerge, there are no known mechanisms for incrementally patching CPS in the event of new attacks. To this end, we develop a novel approach based on recent advances in mitigating CP-attacks using run-time enforcement (RE). RE-methods have been developed for CPS, such as industrial processes and pacemakers. However, the proposed solutions are not developed considering the need for future patching as new attacks emerge. To this end, we develop the first compositional RE framework, which is specifically developed to be able to add new security patches as new security policies are added. We illustrate our approach using the case study of a drone swarm. The experimental results show that the proposed compositional/incremental approach does not suffer from the state space explosion, unlike the monolithic composition. We demonstrate a linear relationship between compile time, compile size, and execution time as the number of policies increases in the proposed compositional scheme.

Sandia National Laboratories and Idaho National Laboratory deployed state-of-the-art cybersecurity technologies within a virtualized, cyber-physical wind energy site to demonstrate their impact on security and resilience. This work was designed to better quantify cost-benefit tradeoffs and risk reductions when layering different security technologies on wind energy operational technology networks. Standardized step-by-step attack scenarios were drafted for adversaries with remote and local access to the wind network. Then, the team investigated the impact of encryption, access control, intrusion detection, security information and event management, and security, orchestration, automation, and response (SOAR) tools on multiple metrics, including physical impacts to the power system and termination of the adversary kill chain. We found, once programmed, the intrusion detection systems could detect attacks and the SOAR system was able to effectively and autonomously quarantine the adversary, prior to power system impacts. Cyber and physical metrics indicated network and endpoint visibility were essential to provide human defenders situational awareness to maintain system resilience. Certain hardening technologies, like encryption, reduced adversary access, but recognition and response were also critical to maintain wind site operations. Lastly, a cost-benefit analysis was performed to estimate payback periods for deploying cybersecurity technologies based on projected breach costs.

Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communication strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication designs. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to targeted use cases. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-unmanned aerial vehicle (UAV) systems, autonomous vehicles, distributed learning systems, smart manufacturing plants, 5G and beyond self-organizing networks, and tactile internet. Finally, this paper also highlights the most important open research topics from both the theoretical framework and application points of view.

This paper presents the cyber-physical system (CPS) of a numerical method (the widely-used Euler-Maruyama method) and establishes a foundational theory of the CPSs of numerical methods for stochastic differential equations (SDEs), which transforms the way we understand the relationship between the numerical method and the underlying SDE. The CPS is a seamless integration of the SDE and the numerical method, unlike in the literature where they are treated as separate systems linked by inequalities. We formulate a new and general class of stochastic impulsive differential equations (SiDEs) that can serve as a canonic form of the CPSs and establish a Lyapunov stability theory as a theoretic foundation for our class of SiDEs. By the CPS approach, we show the equivalence and intrinsic relationship between the stability of the SDE and the stability of the numerical method. As application of our proposed results, we develop the CPS theory for linear systems and present the CPS Lyapunov inequality that is the necessary and sufficient condition for mean-square stability of the CPS of the Euler-Maruyama method for linear SDEs. Our proposed CPS theory initiates the study of systems numerics and provokes many open and interesting problems for future work.

Connected Autonomous Vehicles (CAVs) are transformational technologies that have demonstrated significant potential for shaping the future of transportation systems. CAV research has been conducted extensively, both in academic and industrial pursuits. The intention was to put CAVs on the road, but the safety and efficiency of CAVs must be prioritized for this purpose to come to fruition. This technology is built upon sensors, and network communications have the potential to improve automotive infrastructure, reduce traffic and accidents, and facilitate a unified transportation system. Although the auspiciousness of these vehicles is clear, persistent threats exist in terms of cybersecurity attacks, which jeopardize the safety and effectiveness of CAVs. Our study provides a comprehensive dissection of cyberattacks and digital forensics on CAVs. We begin by discussing each element of a standard CAV network and then illustrate the current security. The three main components of CAVs– sensors, communication networks, and actuators -were analyzed in detail. The expansion of cybersecurity and forensic issues is presented with additional investigations into traditional and artificial intelligence-based cyber-defense techniques. Our work concludes by discussing the open challenges and potential research areas for developing robust cybersecurity and forensic solutions exclusively for CAVs.

This paper investigates the challenging fault prediction problem in process industries that adopt autonomous and intelligent cyber-physical systems (CPS), which is in line with the emerging developments of industrial internet of things (IIoT) and Industry 4.0. Particularly, we developed an end-to-end deep learning approach based on a large volume of real-time sensory data collected from a chemical plant equipped with wireless sensors. Firstly, a novel recursive architecture with multi-lookback inputs is proposed to perform autoregression on imbalanced time-series data as a preliminary prediction. In this process, a novel learning algorithm named recursive gradient descent (RGD) is developed for the proposed architecture to reduce cumulative prediction uncertainties. Subsequently, a classification model based on temporal convolutions over multiple channels with decay effect is proposed to perform multi-class classification for fault root cause identification and localization. The overall network is named the cumulative uncertainty reduction network (CURNet), for its superior capacity in reducing prediction uncertainties accumulated over multiple prediction steps. Performance evaluations show that CURNet is able to achieve superior performance especially in terms of fault prediction recall and fault type classification accuracy, compared to the existing techniques.

Manufacturers are constantly looking to enhance the performance of their manufacturing systems by improving their ability to address disruptions and disturbances, while reducing cost and maximizing quantity and quality. Even though innovative mechanisms for adaptability and flexibility continuously contribute to the smart manufacturing evolutionary process, they generally stop short of providing a capability for coordinated on-line learning. This is especially true when that learning requires exploration outside of established operational boundaries or uses artificial intelligence (as opposed to purely human intelligence) as part of the dynamic implementation of learning. In this work, we provide a vision for the development of an automated learning control architecture to extend the adaptability and flexibility capabilities of manufacturing systems. As part of this vision, we describe a set of requirements and objectives that, if addressed, provide an environment to allow distributed and automated learning across the manufacturing ecosystem. We then provide an example communication and control architecture that meets these requirements and objectives by gathering information, building a dynamic knowledge base, distributing intelligence, making decisions, and adapting the control commands sent to the equipment and people across the manufacturing ecosystem. The example architecture leverages both centralized and distributed control strategies and the ability to switch between the strategies to gather and learn from information in the system. Example case studies are provided illustrating how this architecture can be used to improve manufacturing system performance.

Networks on the cyber-physical systems (CPSs) configure feedback control loops between physical systems in the real world and control software in the cyber world. Malicious behaviors on the networks can increase network delays by exhausting limited network resources and security vulnerabilities to destabilize CPSs, which are entitled the network delay attack. In this paper, we focus on the problem of how to guarantee the stability of CPS under the network delay attack. We propose a real-time controller reconfiguration to ensure the resiliency of the physical systems against the network delay attack. Our controller reconfiguration consists of two algorithms: controller gain tuning and access point (AP) handover, which give a delay tolerance and an attack avoidance, respectively. Depending on the network delays, the computing system adopts one of these two algorithms and mitigates the physical impacts of the network delay attack. We validate that the proposed controller reconfiguration can ensure the resiliency of CPS against the network delay attack by implementing a testbed with wireless networks.

Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities.

This paper studies the problem of the secure state estimation of cyber-physical systems. The system model we studied is a class of discrete-time systems with state delay and sparse sensor attacks. In the design of the observer, a Projection Operator is utilized to determine the correct attack set, and the gain matrix will be switched accordingly. In this way, the excessive estimation error could be avoided effectively. In addition, based on the generalization of the classical Krasovskii Stability Theorem for stability analysis of time-delay systems, a Lyapunov-Krasovskii functional is chosen and a sufficient condition for the existence of the observer together with its design method is determined. The designed observer overcomes the difficulties caused by state delay and sensor attacks, and has good performance in secure estimation. Finally, two examples are given to verify that the observer can restore the desired estimate in a short time when a sudden attack occurs.

Industry 4.0 is a revolution in manufacturing by introducing disruptive technologies such as Internet of Things (IoT) and cloud-computing into the heart of the factory. The resulting increased automation and the improved production synergy between stocks, supply chains and customer demands, come along with the threats and attacks from the Internet. Despite extensive literature on the cybersecurity topic, many actors in manufacturing factories are just realizing the impact of cybersecurity in the preservation of their business. This paper introduces step-by-step the concepts and practical aspects of an Industry 4.0 manufacturing factory that are related to cybersecurity. Based on a subdivision of a typical factory into several generic perimeters, we present the vulnerabilities and threats regarding the network and devices usually found in each perimeter. Therefore, it is more efficient to present the recent proposals of the literature regarding cybersecurity guidelines and solutions in Industry 4.0. Instead of spreading a lot of references regarding every aspect of cybersecurity, we focused on a limited number of papers among the recent references. However, for each paper, we provide the details about the purpose of the proposal, the methodology adopted, the technical solution developed and its evaluation by the authors. These solutions range from classical cybersecurity countermeasures to innovative ones, such as those based on honeypots and digital twins. In order to deliver a review also useful to non scientists, we present our guidelines along with those of some organizations involved in cybersecurity harmonization and standardization in the world.

In recent years the number of security incidents affecting control systems has increased. These incidents have shown the need to develop strategies to improve system resilience to cyber-attacks. This paper presents a practical implementation of a strategy to detect cyber-attacks and mitigate their effects on sensors of a multi-agent system. The proposed approach computes, in real-time, a convex combination of measurements from main and redundant sensors, producing a trust value of the measurement and feeding it to the controller. We implemented this approach on a testbed of ground robots in formation. Experimental results to various kinds of attacks and a key performance index show that the proposed strategy reduces the effects of attacks not only on the affected agent, but also prevents the propagation of the attack over the remaining agents.