Cyber-Physical Systems and their Security Issues

Guest Editorial

Due to the increasing number of threats against Cyber Physical System (CPS) networks, security monitoring in these networks is challenging. Machine learning methods have been widely used to analyse network data and detect intrusions automatically. However, these automated intrusion detection systems (IDSs) are black boxes, and there is no explanation for their decision. Therefore, explainable machine learning techniques can be used to explain the reasons behind the decision made by machine learning-based IDSs. However, there is no sufficient study on explainable IDSs in CPS networks. The other challenge in CPS networks is the growing volume of data. A NetFlow-based analysis is a scalable method suitable for a high volume of data. However, the efficiency of such a method in CPS networks has not been sufficiently investigated. In this chapter, we address these challenges by proposing an explainable NetFlow-based IDS (X-NFIDS) for CPS networks. The Internet of Things (IoT) environment is used as an example of CPS networks. To demonstrate the feasibility of our approach, we perform some preliminary studies of the proposed method using two NetFlow datasets for IoT.

This paper proposes an integration framework for a sensors network in cyber-physical systems. The framework proposed is still an effort in development to ease the integration and management of software and hardware for sensor networks in cyber-physical systems. The parameters, modeling process and implementation of the framework presented in this work correspond to the first step of the modeling. Such step focuses on analysis and treatment of the functional requirements of the uses for the wireless sensor networks combined with a refined vision of the architecture and its main components.

CPS is complex distributed systems, which contain computing, communications, and control. CPS is a product of the combination of physical world and the cyber world. The cyber world needs a lot of physical equipment to deal with perception and communication, then collect and transfer the information in the real environment, and by computation to forecast what might happen in future real environment, at last through the control strategy to achieve the optimal solution. In short, cyber physical system is the complex systems combination with computation system, sensory system and control system. Cyber physical system through more extensive connection, the physical world more thorough cognition, more effectively control the physical world, make the information world and the physical world closer integration, realize coordination awareness and control of the physical world. The paper through the analysis of the human perception system, build a cyber-physical fusion system based on human perception architecture.

Wireless self-organizing adhoc network of cyber physical systems, a kind of machine-to-machine (m2m) communication network connecting different cyber devices, e.g. networks of moving vehicles and mobile facilities (VANET, FANET, MARINET, MANET), industrial Internet of Things (IoT), sensor network of smart building, has become a concern for cyber security for last decade. Every device in this network is a cyber physical system that can independently get commands, do operations, move in the space, and it becomes possible to manage it remotely. For its security sensitivity, this ITC system is required to be protected with an adequate and effective intrusion detection mechanism. Traditional artificial neural networks (ANNs) applied to solve this issue become unhelpful for their computational constrains for big data processing and lack of comprehensive training datasets. The paper explores generative adversarial ANNs to detect security intrusions in large-scale networks of cyber devices. Based on the results of the experiments, an assessment is made of applicability of generative adversarial ANNs to detect security anomalies, and also practical recommendations are discussed for their utilization in the connected cyber physical networks.

The System of Cyber-Physical Systems (SoCPS) comprises several independent Cyber-Physical Systems (CPSs) that interact with each other to achieve a common mission that the individual systems cannot achieve on their own. SoCPS are rapidly gaining attention in various domains, e.g., manufacturing, automotive, avionics, healthcare, transportation, and more. SoCPS are extremely large, complex, and safety-critical. As these systems are safety-critical in nature, it is necessary to provide an adequate safety analysis mechanism for these collaborative SoCPS so that the whole network of these CPSs work safely. This safety mechanism must include composite safety analysis for a network of collaborative CPS as a whole. However, existing safety analysis techniques are not built for analyzing safety for dynamically forming networks of CPS. This paper introduces a composite safety analysis approach called SafeSoCPS to analyze hazards for a network of SoCPS. In SafeSoCPS, we analyze potential hazards for the whole network of CPS and trace the faults among participating systems through a fault propagation graph. We developed a tool called SoCPSTracer to support the SafeSoCPS approach. Human Rescue Robot System—a collaborative system—is taken as a case study to validate our proposed approach. The result shows that the SafeSoCPS approach enables us to identify 18 percent more general faults and 63 percent more interaction-related faults in a network of a SoCPS.

This paper presents definitions characterizing the concepts regarding cyber-physical systems and dependability. Cyber-physical systems incorporate computing, communication and storage capabilities with monitoring and/or control of entities in the physical world in a dependably, securely, efficiently and real-time way. The challenges of cyber-physical system research are concerning: real-time system abstractions; robustness, safety and security; QoS composition; and nor least dependability. Dependability is first introduced as a global concept that subsumes the usual attributes of reliability, availability, safety, integrity and maintainability. The paper aims to define research challenges to achieve the dependability in cyber-physical hydropower systems. The significant challenge of the dependability in cyber-physical hydropower systems is evaluation of the system behavior in terms of interdependencies between cyber and physical components of the system.

Construction of approximate reasoning model for dynamic CPS network and system parameter identification

Security threats and measures for the cyber-physical systems

Security and Privacy Aspect of Cyber Physical Systems

Editorial: This special issue features eight selected papers with high quality. The first article, “A Cost-Effective Methodology Applied to Videoconference Services on Hybrid Clouds” by Javier Cervino, Pedro Rodriguez, Irena Trajkovska, Fernando Escribano and Joaquin Salvachua, tackles the optimization of applications in multi-provider hybrid cloud scenarios from an economic point of view. However the approach was intended to introduce a novel solution by making maximum use of divide and rule. Also this article describes a methodology to create cost aware cloud applications that can be broken down into the three most important components in cloud infrastructures: computation, network and storage. A real videoconference system has been modified in order to evaluate this idea with both theoretical and empirical experiments. The proposed system has become a widely used as a tool in several national and European projects for e-learning and collaboration purposes. The second article, “An Efficient Dynamic Integration Middleware for Cyber-Physical Systems in Mobile Environments” by Young-Sik Jeong, Sang Oh Park and Jong Hyuk Park, proposes a cyber-physical system (CPS) middleware framework that ensures interoperability and communication between heterogeneous components in a global CPS network. A CPS is a tight integration of the system’s computational and physical elements. The CPS technology builds on the older discipline of embedded systems, and CPS applications can be found in diverse industry sectors, such as smart home, health care, and transportation. The article assume that a global CPS network that integrates different CPS networks appears in the near future. Through local and global communications, the proposed middleware makes mobile devices in different networks interoperable. The third article, “Particle Swarm Optimization with Skyline Operator for Fast Cloud-based Web Service Composition” by Shangguang Wang, Qibo Sun, Hua Zou, Fangchun Yang, proposed a fast Cloud-based web service T. Shon (*) Division of Information and Computer Engineering, College of Information Technology, Ajou University, Suwon, South Korea e-mail: taeshik.shon@gmail.com

With a rapidly aging population, the healthcare community will soon face severe medical personnel shortage and rising costs. Pervasive Health Monitoring Systems (PHMS) can help alleviate this situation. PHMS provides continuous real-time monitoring of a person’s health using a (usually wireless) network of medical and ambient sensors/devices on the host (patients), called Body Area Networks (BANs). The sensitive nature of health information collected by PHMS mandates that patient’s privacy be protected by securing the medical data from any unauthorized access. The authors’ approach for addressing these issues focuses on a key observation that PHMS are cyber-physical systems (CPS). Cyber-physical systems are networked, computational platforms, deeply embedded in specific physical processes for monitoring and actuation purposes. In this work, they therefore present a novel perspective on securing PHMS, called Cyber Physical Security (CYPSec) solutions. CYPSec solutions are environmentally-coupled security solutions, which operate by combining traditional security primitives along with environmental features. Its use results in not only secure operation of a system but also the emergence of additional “allied” properties which enhance its overall capabilities. The principal focus of this chapter is the development of a new security approach for PHMS called CYPsec that leverages their cyber-physical nature. The authors illustrate the design issues and principals of CYPSec through two specific examples of this generic approach: (a) Physiological Signal based key Agreement (PSKA) is designed to enable automated key agreement between sensors in the BAN based on physiological signals from the body; and (b) Criticality Aware Access Control (CAAC) which has the ability to provide controlled opening of the system for emergency management. Further, they also discuss aspects such as altered threat-model, increased complexity, non-determinism, and mixed critical systems, that must be addressed to make CYPSec a reality.

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

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 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.