Security Of Cyber-physical Systems Research Articles

Design Hands-on Lab Exercises for Cyber-physical Systems Security Education

The integration of cyber-physical systems (CPS) has been extremely advantageous to society, it merges the attention of cybersecurity for vehicles as a timely concern as a matter of public and individual. The failure of any vehicle system could have a serious impact on vehicle control and cause undesired consequences. With the growing demand for security in CPS, there are few hands-on labs/modules available for training current students, future engineers, or IT professionals to understand cybersecurity in CPS. This study describes the execution of a free security testbed to replicate a vehicle’s network system and the implementation of this testbed via hands-on lab designed to introduce concepts of vehicle control systems. The hands-on lab simulates insider threat scenarios where students had to use can-utils toolkits and SavvyCAN to send, modify, and capture the network packet and exploit the system vulnerability threats such as replay attacks and fuzzing attacks on the vehicle system. We conducted a case study with 21 university-level students, and all students completed the hands-on lab, pretest, posttest, and a satisfaction survey as part of a non-graded class assignment. The experimental results show that most students were not familiar with cyber-physical systems and vehicle control systems and never had the chance to do any hands-on lab in this field before. Furthermore, students reported that the hands-on lab helped them learn about CAN-bus and rated high scores for enjoyment. We discussed the design of an affordable tool to teach about vehicle control systems and proposed directions for future work.

Data Mining for the Security of Cyber Physical Systems Using Deep-Learning Methods

Cyber Physical Systems (CPSs) have become widely popular in recent years, and their applicability have been growing exponentially. A CPS is an advanced system that incorporates a computation unit along with a hardware unit, allowing for computing processes to interact with the physical world. However, this increased usage has also led to the security concerns in them, as they allow potential attack vendors to exploit the possibilities of committing misconduct for their own benefit. It is of paramount importance that these systems have comprehensive security mechanisms to mitigate these security threats. A typical attack vector for a CPS is malicious data supplied by compromised sensors that are part of the CPSs. To combat this attack vector, many systems are secured through fault tolerance, including methods such as checkpointing to recover the system. Looking at the diverse nature of attacks and their ever growing complexities, traditional security approaches may not counter them efficiently, which creates a vacuum to be filled with sophisticated state-of-the-art techniques. In this paper, Deep Learning methods such as autoencoders, and Support Vector Machines are proposed to secure CPSs against these attacks. The networks in these applied methods are trained with a normal data profile devoid of any malicious data. Data collected from the system’s sensors at specified intervals is used to form a data series and input to the neural networks. The networks compare and analyze new data to the normal profile to detect anomalies, if there is any. In the presence of anomalous data, the networks generate corrective action(s) for these sensors and the physical states they are recording. Through detection of anomalies, effective security of CPSs may be improved in addition to providing protection for the sensors. Moreover, the proposed method of securing CPSs opens up the possibility of further research by showcasing the applicability of neural networks in securing CPSs.

Critical Systems Protection (CSP): The US Secret Service’s Tactical Cyber Capability for Securing Protectees

This paper examines a largely unknown cybersecurity function within the United States Secret Service (USSS), that is separate and apart from its traditional cybersecurity mission in financial crime investigations. This program - Critical Systems Protection (CSP) – is instead focused on the Secret Service’s protective mission and analyzes and secures the cyber environments of facilities and locations visited by the agency’s protectees. Exploiting publicly available documents about CSP, this paper will document this unique organization that combines some of the highest profile issues in cybersecurity – operational and tactical level cybersecurity efforts, and the security of cyber-physical systems. Drawing on open-source information and government documents, this paper sketches out the CSP mission, its partnerships with other agencies and organizations, and some of the tools and other capabilities it has developed.

Diversity-by-Design for Dependable and Secure Cyber-Physical Systems: A Survey

Diversity-based security approaches have been studied for several decades since the 1970's. The concept of diversity-by-design emerged in the 1980's and, since then, diversity-based system design research has been explored to build more secure and dependable systems. In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, insights, and future work directions for those who want to conduct research on developing secure and dependable cyber-physical systems (CPSs) using diversity as a system design feature. To be specific, this survey paper provides: (i) The common concept of diversity based on a multidisciplinary study of diversity from nine different fields along with the historical evolution of diversity-by-design for security; (ii) The design principles of diversity-based approaches; (iii) The key benefits and caveats of using diversity-by-design; (iv) The key concerns of CPS environments in introducing diversity-by-design; (v) A variety of existing diversity-based approaches based on five different classifications; (vi) The types of attacks mitigated by existing diversity-based approaches; (vii) The overall trends of evaluation methodologies used in diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) The insights, lessons, and gaps identified from this extensive survey.

Event-Based Fault Diagnosis of Networked Discrete Event Systems

The problem of fault diagnosis has become an active research topic in Discrete Event Systems, which can be applied into many different areas, such as communication networks, process control, flexible manufacturing systems, software engineering, etc. This brief explores the effect of communication delays on the event-based fault diagnosis in networked discrete event systems (NDESs), which can be modeled by finite state automata. Firstly, the framework of NDESs is introduced, and a novel concept of networked diagnosability ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Net$ </tex-math></inline-formula> -diagnosability) is formally defined. Subsequently, the necessary and sufficient conditions for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Net$ </tex-math></inline-formula> -diagnosability in the partially observable outputs are provided, and the corresponding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Net$ </tex-math></inline-formula> -diagnosability can be verified by an effective diagnoser, which can be obtained by a constructed algorithm. Finally, an illustrative example is given to demonstrate the proposed method. The current results about networked diagnosability will be conducive to maintain the information security in the actual cyber-physical systems.

A Game Theory approach for communication security and safety assurance in cyber-physical systems with Reputation and Trust-based mechanisms

Cyber-physical systems’ security and safety assurance is a challenging research problem for Smart City concept development. Technical faults or malicious attacks over communication between its elements can jeopardize the whole system and its users. Reputation systems implementation is an effective measure to detect such malicious agents. Each agent in the group has its indicator, which reflects how trustworthy it is to the other agents. However, in the scenario when it is not possible to calculate the Reputation indicator based on objective characteristics, malicious or defective agents can negatively affect the system’s performance. In this paper, we propose an approach based on Game Theory to address the Reputation and Trust initial values calculation challenge. We introduced a mixed strategies game concept and a probability indicator. The possible outcomes of using different strategies by the system agents are represented with a payoff matrix. To evaluate the approach effectiveness, an empirical study using a software simulation environment was conducted. As a Cyber-physical system implementation scenario, we considered an intersection management system with a group of unmanned autonomous vehicles, the aim of which is to perform conflict-free optimal intersection traversal. To simulate the attack scenario, some vehicles were able to transmit incorrect data to other traffic participants. The obtained results showed that the Game Theory approach allowed us to increase the number of detected intruders compared to the conventional Reputation and Trust model.

Cyber-Physical Systems and Cyber Security: A Bibliometric Analysis

With the emergence of Industry 4.0, the use of advanced technologies has become widespread in both the manufacturing and service sectors. Among the most important of the advanced technologies used are cyber physical systems (CPS). Along with the use of technology, security has also become highly important. For this reason, along with CPSs, the issue of cyber security has also developed. For this purpose, in this study, bibliometric analysis of 827 studies published between 1999-2021 in the field of CPS and cyber security in journals scanned in Web of Science was performed. With the thematic evolution analysis, the change in the field has been revealed based on time. While the basic elements of cyber security issues were discussed in the period of 1999-2015, which was determined as the first period, issues such as smart grids and education were observed in the period of 2016-2019, which was determined as the second period. In the last period of 2020-2021, topics such as cloud computing, game theory and maritime cyber security have emerged. However, there are also CPSs and cyber security concepts that exist in all three periods. From this point of view, it can be concluded that the field of CPS and cyber security is not yet fully mature and more detailed studies are needed on basic issues, while interdisciplinary studies are expected to gain weight in the coming days, as it is a subject that has an impact on many different dimensions.

Deep Learning Based Distributed Intrusion Detection in Secure Cyber Physical Systems

Cyber Physical Systems (CPSs) are network systems containing cyber (computation, communication) and physical (sensors, actuators) components that interact with each other through feedback loop with the help of human intervention. The dynamic and disseminated characteristics of CPS environment makes it vulnerable to threats that exist in virtualization process. Due to this, several security issues are presented in CPS. In order to address the challenges, there is a need exists to extend the conventional security solutions such as Intrusion Detection Systems (IDS) to handle high speed network data traffic and adaptive network pattern in cloud. Additionally, the identification of feasible network traffic characteristics is the main issue in precise detection of attacks in the network. With this motivation, the current research paper presents an Optimal Deep Belief Network-based distributed Intrusion Detection System (ODBN-IDS) for secure CPS environment. The proposed model pre-process the cloud network traffic data to improve its quality to next level. Here, a Binary Flower Pollination Algorithm (BFPA) is employed for feature selection process. The attained characteristics are used in optimal Deep Belief Networks (DBN) to detect the presence of intrusion in cloud data and produce alarms, in case of presence of intrusions. Equilibrium Optimizer Algorithm (EOA) is used to fine tune the hyperparameters in DBN model. A detailed set of simulations was conducted on benchmark datasets and the analysis results were compared. A detailed comparison was conducted for various models to satisfy the security requirements of cloud network and the results established the supremacy of the proposed ODBN-IDS model.

Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems. The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care. Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector. So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy. To overcome the challenges, the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS (DLIDIC-SCPS) model for healthcare sector. The aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare sector. Primarily, data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network (FDNN) technique. Besides, Multiple Share Creation (MSC) approach is used to create several shares of medical image so as to accomplish security. Also, blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client. For image classification, Inception v3 with Fuzzy Wavelet Neural Network (FWNN) is utilized that diagnose the disease from the applied medical image. Finally, Salp Swarm Algorithm (SSA) is utilized to fine tune the parameters involved in WNN model, thereby boosting its classification performance. A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS technique. The simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security, privacy, and image classification outcomes over recent state-of-the-art techniques.

Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems

Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems

Problems and solutions regarding generalized functional safety in cyberspace

The common endogenous security problems in cyberspace and related attack threats have posed subversive challenges to conventional theories and methods of functional safety. In the current design of the cyber physical system (CPS), functional safety and cyber security are increasingly intertwined and inseparable, which evolve into the generalized functional safety (S&amp;S) problem. The conventional reliability and cybersecurity technologies are unable to provide security assurance with quantifiable design and verification metrics in response to the cyberattacks in hardware and software with common endogenous security problems, and the functional safety of CPS facilities or device has become a frightening ghost. The dynamic heterogeneity redundancy (DHR) architecture and coding channel theory (CCT) proposed by the cyberspace endogenous security paradigm could handle random failures and uncertain network attacks in an integrated manner, and its generalized robust control mechanism can solve the universal problem of quantitative design for functional safety under probability or improbability perturbation. As a generalized functional safety enabling structure, DHR opens up a new direction to solve the common endogenous security problems in the cross-disciplinary fields of cyberspace.

Security of Multi-Agent Cyber-Physical Systems: A Survey

Multi-agent systems are becoming increasingly popular due to their successful implementation in several sectors. However, there are a variety of threats that might undermine the agent’s security and imperil system security. As a result, security concerns should be addressed during the design of multi-agent systems. This survey reviews different models for securing multi-agent systems, which were developed based on the concepts regarding the agent’s role and communications. This paper presents and categorizes the most common attacks on MASs. Then, we study and analyze numerous security strategies in the literature, classifying them as prevention, detection, and resiliency approaches based on reputation and trust. Finally, we recommend which security approach is the best countermeasure for specific types of attacks based on recent advances in the research field.

Security and Privacy Challenges in the Deployment of Cyber-Physical Systems in Smart City Applications: State-of-Art Work

The concept “Smart City” is realized by incorporating advanced sensing and computational technologies in various processes, services, and infrastructure. Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the key role players in the major development of smart city infrastructure. Further, the CPS stationing has improved the quality of various services like healthcare, transportation, environmental, etc., provided in a typical smart city ecosystem. But, the embodiment of CPS has also increased the surface area for cyber-attacks. In this paper, the various security and privacy issues at the different levels in the architecture of a smart city are discussed. The domain-centric security issues evolved with the deployment of CPS in transportation, healthcare, smart grids, and smart homes are also explored. Scopus publications for the years 2016–2020 are considered for the bibliometric analysis in security. It has been observed that a major number of security solutions have been proposed in the domain of smart homes during these years, whereas much scope is still there to address security and privacy challenges for CPS equipped smart grids. But over time, the security of smart grids is also becoming an area of interest for researchers. The article is concluded with the research gaps and future scope to cater secure smart city services.

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

Recently, cyber physical system (CPS) has gained significant attention which mainly depends upon an effective collaboration with computation and physical components. The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems (CPES). At the same time, the rising ubiquity of wireless sensor networks (WSN) in several application areas makes it a vital part of the design of CPES. Since security and energy efficiency are the major challenging issues in CPES, this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms (EASCPS-MA). The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection. The EASCPS-MA technique encompasses two main stages namely improved fruit fly optimization algorithm (IFFOA) based clustering and optimal deep stacked autoencoder (OSAE) based intrusion detection. Besides, the optimal selection of stacked autoencoder (SAE) parameters takes place using root mean square propagation (RMSProp) model. The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects. The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

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.

Development of cyber-physical system security management process model

Cyber-physical systems (CPS) combine general-purpose information and telecommunication systems and industrial networks and devices (controllers), forming a heterogeneous hierarchical distributed information technology computing environment designed for control and monitoring of technological processes. FSCs are being actively implemented in the industrial, urban and household spheres, allowing for more efficient use of resources, as well as transferring management and feedback processes to a new level. CPS are formed as a result of the aggregation of several heterogeneous information and telecommunication systems, which makes them susceptible to the destructive factors of the digital environment – cyber attacks. The peculiarities of the construction and functioning of the CPS require a new approach to defining attack surfaces and modelling threats. This is due to the fact that the "traditional" goals of attackers - the availability, integrity and confidentiality of information processed in the system - are not the only ones. In addition, the value of the information itself can be extremely low. At the same time, control and feedback processes play a decisive role in a cyber-physical system. Their stability directly affects the ability of the FSC to perform target functions, as well as the physical safety of technological facilities, personnel and consumers. Depending on the degree of criticality of the technological processes of the real world, which are controlled by the cyber-physical system, the violation of these flows inside it can lead to serious consequences, such as large material damage or a threat to the life and health of people. The severity of the identified threats is confirmed by the constantly growing number of attacks on CPS and the consequences, including potential ones, to which these attacks lead. In addition, it should be noted that in the context of the rapid development and improvement of programmable control devices (Programmable Logic Controller (PLC)), making them more accessible in the mass market segment, as well as the proliferation of high-speed wireless data transmission networks, the concept of a smart home is gaining wider application. This approach implies the integration of PLCs that interact with each other and the control centre via multi-service broadband wireless networks into the domestic environment. Thus, the concept of a smart home is based on the implementation of FSCs in places of permanent residence of people with the transfer of automatic control functions of climatic equipment, lighting, household appliances, physical elements of protection against intrusions to them. At the same time, it is extremely important to ensure the information security of the CFS. To ensure the required level of their security, it is necessary to effectively solve the problems of managing the process of ensuring their information security. The article proposes an approach to the development of a model for the information security management process of a CFS operating in a smart home.

Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML) models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication. In this aspect, this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder (XAIIDS-FSDVAE) model for CPS. The proposed model encompasses the design of coyote optimization algorithm (COA) based feature selection (FS) model is derived to select an optimal subset of features. Next, an intelligent Dirichlet Variational Autoencoder (DVAE) technique is employed for the anomaly detection process in the CPS environment. Finally, the parameter optimization of the DVAE takes place using a manta ray foraging optimization (MRFO) model to tune the parameter of the DVAE. In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique, a wide range of simulations take place using the benchmark datasets. The experimental results reported the better performance of the XAIIDS-FSDVAE technique over the recent methods in terms of several evaluation parameters.

Key Concepts of Systemological Approach to CPS Adaptive Information Security Monitoring

Modern cyber-physical systems (CPS) use digital control of physical processes. This allows attackers to conduct various cyberattacks on these systems. According to the current trends, an information security monitoring system (ISMS) becomes part of a security management system of CPS. It provides information to make a decision and generate a response. A large number of new methods are aimed at CPS security, including security assessment, intrusion detection, and ensuring sustainability. However, as a cyber-physical system operates over time, its structure and requirements may change. The datasets available for the protection object (CPS) and the security requirements have become dynamic. This dynamic effect causes asymmetry between the monitoring data collection and processing subsystem and the presented security tasks. The problem herein is the choice of the most appropriate set of methods in order to solve the security problems of a particular CPS configuration from a particular bank of the available methods. To solve this problem, the authors present a method for the management of an adaptive information security monitoring system. The method consists of solving a multicriteria discrete optimization problem under Pareto-optimality conditions when the available data, methods or external requirements change. The experimental study was performed on an example of smart home intrusion detection. In the study, the introduction of a constraint (a change in requirements) led to the revision of the monitoring scheme and a different recommendation of the monitoring method. As a result, the information security monitoring system gains the property of adaptability to changes in tasks and the available data. An important result from the study is the fact that the monitoring scheme obtained using the proposed management method has a proven optimality under the given conditions. Therefore, the asymmetry between the information security monitoring data collection and processing subsystem and the set of security requirements in cyber-physical systems can be overcome.

A Novel System-Theoretic Matrix-Based Approach to Analysing Safety and Security of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are getting increasingly complex and interconnected. Consequently, their inherent safety risks and security risks are so intertwined that the conventional analysis approaches which address them separately may be rendered inadequate. STPA (Systems-Theoretic Process Analysis) is a top-down hazard analysis technique that has been incorporated into several recently proposed integrated Safety and Security (S&amp;S) analysis methods. This paper presents a novel methodology that leverages not only STPA, but also custom matrices to ensure a more comprehensive S&amp;S analysis. The proposed methodology is demonstrated using a case study of particular commercial cloud-based monitoring and control system for residential energy storage systems.

An approach to the identification of the state of elements in cyber-physical systems based on principal component analysis

The close integration of modern cyber-physical systems with production and technological ones as well as with critical information infrastructure requires improving the monitoring process. The monitoring process is necessary with a constant increase in the possible points of entry into the system. The processing of a large amount of data coming from monitoring systems requires significant computing power. In this regard, it is relevant to reduce the dimension of the feature space while maintaining an acceptable monitoring accuracy. The proposed solution should be invariant to the dimension and orders of magnitude from which the time series supplied to the input of the monitoring system are composed. To obtain the most informative features in the formation of their set, it is proposed to use principal component analysis, and a method based on decision trees for their classification. A computational experiment was performed to confirm the applicability of the developed approach. The data of the network traffic for the research stand of the cyber-physical system water treatment were used in the experiment. The accuracy of the set of methods on the analyzed data was 98.74 %. The comparison with known studies showed an increase in the F-measure up to 0.925, which is 4.8 % higher than the most effective method used to date, namely the Isolation Forest method. The developed approach allows one to significantly increase the speed of identification and to detect anomalies of information security and functional safety of cyber-physical systems with high accuracy by reducing the dimension of the original feature space. The proposed approach can be used in event monitoring systems that deal with information security. The presented theoretical results can be useful for researchers of information security and functional safety of cyber-physical systems.