A Through Analysis on Protecting Cyber Threats and Attacks on Cps Embedded Subsystems

Security and Privacy Aspect of Cyber Physical Systems

Guest Editorial

Cyber Physical Systems (CPS) are characterized by their ability to integrate\nthe physical and information or cyber worlds. Their deployment in critical\ninfrastructure have demonstrated a potential to transform the world. However,\nharnessing this potential is limited by their critical nature and the far\nreaching effects of cyber attacks on human, infrastructure and the environment.\nAn attraction for cyber concerns in CPS rises from the process of sending\ninformation from sensors to actuators over the wireless communication medium,\nthereby widening the attack surface. Traditionally, CPS security has been\ninvestigated from the perspective of preventing intruders from gaining access\nto the system using cryptography and other access control techniques. Most\nresearch work have therefore focused on the detection of attacks in CPS.\nHowever, in a world of increasing adversaries, it is becoming more difficult to\ntotally prevent CPS from adversarial attacks, hence the need to focus on making\nCPS resilient. Resilient CPS are designed to withstand disruptions and remain\nfunctional despite the operation of adversaries. One of the dominant\nmethodologies explored for building resilient CPS is dependent on machine\nlearning (ML) algorithms. However, rising from recent research in adversarial\nML, we posit that ML algorithms for securing CPS must themselves be resilient.\nThis paper is therefore aimed at comprehensively surveying the interactions\nbetween resilient CPS using ML and resilient ML when applied in CPS. The paper\nconcludes with a number of research trends and promising future research\ndirections. Furthermore, with this paper, readers can have a thorough\nunderstanding of recent advances on ML-based security and securing ML for CPS\nand countermeasures, as well as research trends in this active research area.\n

Cyber Physics Systems consists of physical systems and information systems. Modern power systems are increasingly dependent on information systems, experts and scholars are paying more and more attention to cyber attacks on CPS. This paper first introduces the existing research on cyber physical power system. Then this paper reviews the current status of existing cyber attacks. Finally, the modeling method for CPS is summarized such as correlation characteristic matrix, graph theory, complex network theory, finite state machine, mathematical programming and cellular automata method. These methods have vital theoretical and practical significance for the research of cyber physical power system.

Cyber-physical systems (CPS) security has become an increasingly important research topic in recent years. Geared towards more advanced cyberattack detection techniques as part of strategies for enhancing the security of CPS, in this paper we propose a machine learning based cyber-attack detection scheme. The proposed scheme is a physical-domain technique; specifically, it assumes the physical measurements of the system carry sufficient information for capturing the system behavior, thus can be used for differentiating normal operation and attacks. CPS are complex in nature and the number of physical measurements available for CPS is often overwhelmingly high. Thus, accurately modeling CPS’ dynamic behavior, more importantly, distinguishing normal and adversary activities based on the large number of physical measurements, can be challenging. To address the challenge, we have focused our research effort on feature engineering, that is, to intelligently derive a set of salient signatures or features from the noisy measurements. We make sure the derived features are more compact and, more importantly, have more discriminant power than the original physical measurements, thus enabling us to achieve more accurate and robust detection performance. To demonstrate the effectiveness of the proposed scheme, in our experimental study we consider gas turbines of combined cycle power plants as the cyber-physical system. Using the data from the high-fidelity simulation we show that our proposed cyberattack detection scheme is able to achieve high detection performance.

Naval forces rely heavily on Cyber-Physical Systems (CPS) to conduct operations and achieve mission success. As CPS infrastructure becomes exposed to the contested world through networks, CPS security becomes much more important. In a CPS, the cyber components manage the physical components. ONR proposes that the overall goal for CPS resiliency is to have the physical systems behave properly regardless of fault and disruption. Our approach to CPS resiliency focuses on the physical components. We observed that the inertia of the physical components provides a natural but limited resilience, and is capable of tolerating short-term disruption without affecting the health and safety of the CPS. This and the fact that a CPS has a large difference between physical and cyber time scales, enables a unique approach to CPS resiliency. We present Byzantine Fault Tolerant++ (BFT++), a cyber resilient architecture that engineers the cyber components to be brittle against attack, which consequently forces cyber attacks and related disruptions to be short-lived and within tolerance of the physical system’s inertia.

In a time where technologies come and go, when most of the areas of our lives are rely one way or another on electronic devices and the internet, security is a crucial aspect, always needed. Technologies may rise and fall, but the area of security remains necessary at all times. For this reason the research for new algorithms, new and improved methods is constant. Cyber-physical systems are designed as a network of interconnected devices with physical input and output and represent a new direction in the information systems world. In the present paper we present a solution for the security of communication in cyber-physical systems with hierarchical access by combining cryptography and steganography.

The growing requirement for the rise of production, quality, and efficiency of industrial products drives humans to jointly develop novel technologies capable of following up with the exponential technology growth they are facing today in production processes. In the 21st century, industrial internet of things (IIoT) is dedicated to endorsing the internet of things (IoT) to allow the interconnection of anywhere, any time, and anything to enhance efficiency, productivity, intelligence, and safety in the manufacturing system. Consequently, the combined IoT and cyber physical system (CPS) in IIoT will have an influence on industrial techniques through the industrial 4.0 confluence to encourage connected factories. CPS effectively combines physical and cyber components employing modern components such as sensors, network, and computing technologies. But various securities challenges rising in the CPS, like increasing cyberattacks due to the increase in IoT devices, modelling of security threads, CPS vulnerabilities assessment development techniques, and designing of fault-tolerant architectures, motivate new technologies to meet CPS requirements in order to offer security, confidentiality, and reliability of users’ data. Thus, this chapter presents the classification of various existing studies based on CPS security in terms of IIoT applications. Also, it provides the limitations, shortcomings, CPS architectures, and future potential applications to help researchers in the context of practical work.

Due to digitalization and technological advancement, systems and their requirements are changing, and there is an increasing use of Cyber-Physical Systems (CPS) with a direct connection between the physical and the digital world. These systems process data and have integrated functions and a real-time requirement. There is a great need for security, protection of data, and reliability. The use of digital systems in the energy sector is increasing and changing, as are consumers and generators. This requires a secure IT, communications infrastructure, and highly performing data platforms. The new systems being created are called CPS, which are highly scalable, dynamic, and volatile and process many data of various kinds. One significant aspect of a CPS is security. Personal data and business-sensitive data may be processed, or mission-critical processes may be mapped. Risk analysis and security assessments based on conventional methods and guidelines (for example, BSI IT Basic Protection) have revealed drawbacks. Present security assessment methods focus on analyzing corporate information systems or are applied for software development life cycles. CPS criteria and their impact on security have not yet been accounted for in today’s security assessments and their corresponding frameworks. This thesis concentrates on modeling CPS security and deriving a framework for CPS security assessments. The considered criteria are data security as conventional, expanded by scalability, and real-time. The underlying framework is process-oriented. CPS use cases will be broken down into (atomic) processes and the security assessed based on each process’ data security, scalability and real-time model. Eventually, this will mean security measures can be mapped at the process level. Conducting this research, the focus was on smart grid systems as one example of CPS. For the discussion of mapping security measures, authentication was selected. The result analysis shows an added value in the security assessment of CPS based on the criteria of data security, scalability, real-time, and the breakdown at the process level. The underlying model allows to cope with the complexity of CPS and more precisely assess the security of CPS. The overall approach of CPS security modeling and provision by using a process-oriented framework is highly innovative and provides a concept for developing future CPS security assessment tools.

<abstract><p>Due to the complexities of systems thinking and the communication between independent Cyber-Physical Systems (CPSs) areas through accumulative expansion, several security threats are posed, such as deception of channels for information sharing, hardware aspects and virtual machines. CPSs have become increasingly complex, sophisticated, knowledgeable and fully independent. Because of their complex interactions between heterogeneous virtual and objective components, CPSs are subject to significant disturbances from intended and unintended events, making it extremely difficult for scientists to predict their behavior. This paper proposes a framework for Cyber-Physical Business Systems based on Artificial Intelligence (CPBS-AI). It summarizes several safety risks in distinct CPS levels, their threat modeling and the scientific challenges they face in building effective security solutions. This research provides a thorough overview of current state-of-the-art static capable of adapting detection and tracking approaches and their methodological limitations, namely, the difficulty of identifying runtime security attacks caused by hibernation or uncertainty. The way of identifying the threat and the security attacks in networks reduce the complexities in the communication in CPS. The negligible threats exhibit an inability to be identified, avoided and blocked by Intrusion Prevention Security Systems (IPSSs), and misbehavior in the database of the safety measures is analyzed. Neural Networks (NN) and Variable Structure Control (VSC) are designed to estimate attacks and prevent the risk of threats in tracking applications using a nonlinear monitoring system based on VSC. NN and the VSC evaluate the different attacks based on the nonlinear monitoring system. The evaluation of the proposed CPBS-AI is based on the request time analysis, accuracy, loss and reliability analysis. The overall effectiveness of the system is about 96.01%.</p></abstract>

Cyber-physical production systems are highly flexible systems that enable adaptive production processes. In these systems, all participants of the production process possess individual information about themselves and are equipped with sensors, actors, and communication interfaces. They can interact with each other and autonomously develop and execute process relevant decisions. For each component, a standard process sequence and alternative process sequences are defined. If a deviation in the standard process occurs during the production of an individual component, the process participants can interact with each other and autonomously define an appropriate response strategy and execute it by using actors of the participants and the intralogistics. Regardless of the deviation, the manufacturing and assembly process of individual components in cyber-physical production systems can still proceed. For this purpose, process deviations and the response behavior of cyber-physical production systems are analyzed, modeled, and simulated, to illustrate the benefits of cyber-physical production systems and to develop a process deviation management system for actual, physical production systems based on cyber-physical systems.

International audience

Cyber Physical Systems (CPS) are redefining modern engineering practices by bringing the computational systems together with physical processes, boosting efficiency, security, and sustainability in different industries. This research highlights the influence of CPS in different sectors: its power to facilitate realtime decision making, predictive maintenance, and cybersecurity issues. The context in which this study occurs is to the application of digital twin technology in CPS — which is physical systems’ simulation for performance optimization and downtime reduction. The research includes the analysis of the efficiency of cybersecurity measures applied in CPS environment, the identification of vulnerabilities, and the suggestions of solutions for the mitigation of a cyber threat. Digital twins and predictive maintenance appears to improve the system performance by 30%, reduces the downtime by 25% due to application. Moreover, the research addresses upcoming concerns of sustainable practices in CPS and emerging technologies such as Industry 4.0 that promote resource optimization and waste reduction. The results show that CPS can improve both the operational efficiency and the environmental sustainability by enabling a smarter resource management. This work advances our understanding of trends, challenges and future directions in CPS as a means to inform engineering management practices, which are enabled by CPS.

A language for describing attacks on cyber-physical systems

Cyber-physical systems are an essential and frequently used infrastructure for resolving today's most challenging problems. As we all know, making rapid and accurate decisions in a big data setting (also called a critical/significant environment) is a tough challenge. This chapter's potential investigates the specifics of one of the most significant technological revolutions developing "Cyber-Physical Systems" and the increasing role of artificial intelligence techniques in these systems. Artificial intelligence techniques are essential in information security because they can rapidly evaluate millions of incidents and recognize various threats – from malware leveraging zero-day vulnerabilities to risky actions that might result in a phishing attack or download of malicious code. We conducted an efficient search in Web of Science, PubMed, Scopus, and EBSCO for articles published up to April 2021 that addressed federated learning, deep learning, machine learning, graph-based approaches, intrusion detection tree approaches, and Signature Based Malicious Behavior Detection in cyber-security. Additionally, the work compared various attack detection techniques in cyber-physical systems against associated challenges and quality metrics such as accuracy, bandwidth, Variance of Noise, Sparsity rate, Pushing recall, F1-score, precision, and recall. It extensively discusses the context for artificial intelligence in cyber-security, including the different cyber-physical security attacks. The unique aspect of this work is that the survey summarizes current concepts and their limitations, focusing on future research potential in artificial intelligence techniques for Cyber-Physical Systems. This research work would facilitate multiple researchers and scholars investigating cyber-physical domains and serve as a basis for further studies. Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Cyber-Physical Systems; Cyber Security; Attacks; Malware