With the booming of cyber attacks and cyber criminals against cyber-physical systems (CPSs), detecting these attacks remains challenging. It might be the worst of times, but it might be the best of ...times because of opportunities brought by machine learning (ML), in particular deep learning (DL). In general, DL delivers superior performance to ML because of its layered setting and its effective algorithm for extract useful information from training data. DL models are adopted quickly to cyber attacks against CPS systems. In this survey, a holistic view of recently proposed DL solutions is provided to cyber attack detection in the CPS context. A six-step DL driven methodology is provided to summarize and analyze the surveyed literature for applying DL methods to detect cyber attacks against CPS systems. The methodology includes CPS scenario analysis, cyber attack identification, ML problem formulation, DL model customization, data acquisition for training, and performance evaluation. The reviewed works indicate great potential to detect cyber attacks against CPS through DL modules. Moreover, excellent performance is achieved partly because of several high-quality datasets that are readily available for public use. Furthermore, challenges, opportunities, and research trends are pointed out for future research.
Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of ...energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems.
Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to ...transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This article is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this article, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.
New software development patterns are emerging aiming at accelerating the process of delivering value. One is Continuous Experimentation, which allows to systematically deploy and run instrumented ...software variants during development phase in order to collect data from the field of application. While currently this practice is used on a daily basis on web-based systems, technical difficulties challenge its adoption in fields where computational resources are constrained, e.g., cyber–physical systems and the automotive industry.
This paper aims at providing an overview of the engagement on the Continuous Experimentation practice in the context of cyber–physical systems.
A systematic literature review has been conducted to investigate the link between the practice and the field of application. Additionally, an industrial multiple case study is reported.
The study presents the current state-of-the-art regarding Continuous Experimentation in the field of cyber–physical systems. The current perspective of Continuous Experimentation in industry is also reported.
The field has not reached maturity yet. More conceptual analyses are found than solution proposals and the state-of-practice is yet to be achieved. However it is expected that in time an increasing number of solutions will be proposed and validated.
•Continuous Experimentation on cyber–physical systems is desirable but challenged.•The state-of-the-art focuses more on the challenges than on solution proposals.•The majority of literature comprises conceptual studies and empirical investigations.•A solid state-of-practice has not been achieved yet.
With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields ...are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.
The exponential growth of information and communication technologies have caused a profound shift in the way humans engineer systems leading to the emergence of closed-loop systems involving strong ...integration and coordination of physical and cyber components, often referred to as cyber-physical systems (CPSs). Because of these disruptive changes, physical systems can now be attacked through cyberspace and cyberspace can be attacked through physical means. The paper considers security and resilience as system properties emerging from the intersection of system dynamics and the computing architecture. A modeling and simulation integration platform for experimentation and evaluation of resilient CPSs is presented using smart transportation systems as the application domain. Evaluation of resilience is based on attacker-defender games using simulations of sufficient fidelity. The platform integrates 1) realistic models of cyber and physical components and their interactions; 2) cyber attack models that focus on the impact of attacks to CPS behavior and operation; and 3) operational scenarios that can be used for evaluation of cybersecurity risks. Three case studies are presented to demonstrate the advantages of the platform: 1) vulnerability analysis of transportation networks to traffic signal tampering; 2) resilient sensor selection for forecasting traffic flow; and 3) resilient traffic signal control in the presence of denial-of-service attacks.
Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a ...quite challenging task because of the complexity associated with them. SC is modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration as well as service costs. It also improves system efficiency and reduces the associated security risks. However, SCs are well encouraging the new technological reforms in Industry 4.0, but still, various security and privacy challenges need to be addressed. In this paper, a survey on SC security vulnerabilities in the software code that can be easily hacked by a malicious user or may compromise the entire BC network is presented. As per the literature, the challenges related to SC security and privacy are not explored much by the authors around the world. From the existing proposals, it has been observed that designing a complex SCs cannot mitigate its privacy and security issues. So, this paper investigates various Artificial Intelligence (AI) techniques and tools for SC privacy protection. Then, open issues and challenges for AI-based SC are analyzed. Finally, a case study of retail marketing is presented, which uses AI and SC to preserve its security and privacy.
In this article, a defense method with watermarking to detect linear deception attack under Kullback-Leibler (K-L) divergence detector in cyber-physical system (CPS) is proposed. It is known that ...linear deception attacks can reduce the performance of remote estimator without being detected by the K-L divergence detector. In order to detect this kind of attack, we use watermarking to encrypt and decrypt data transmitted through wireless networks. When the attack does not exist, the transmitted data can be restored to ensure the remote estimation performance. In the presence of linear deception attacks, these data are marked with a watermarking so that they can assist the K-L divergence detector to discover the attack. The watermarking encryption method is proved to be helpful for K-L divergence detector to discover attack, or weaken the impact of the attack in different situations. Finally, numerical simulations are provided to further illustrate the results.
•The most important habilitating technologies for Industry 4.0 and Smart Manufacturing are presented.•Trends are discussed.•Basic concepts are defined to contextualize further discussion.
Industry ...4.0 refers to the integration of a multiplicity of technologies and agents for the common goal of improving the efficiency and responsiveness of a production system. This integration has the potential to revolutionize the manner in which business are planned and conducted. Smart Manufacturing represents the implementation of Industry 4.0 on the manufacturing floor. The Internet of Things, Big Data, Cyber Physical Systems, Machine Learning, Additive Manufacturing, and Robotics are only some of the elements that are associated with this revolution. This article discusses trends in some of the habilitating technologies of Industry 4.0.
Cyber-physical systems (CPSs), which are an integration of computation, networking, and physical processes, play an increasingly important role in critical infrastructure, government and everyday ...life. Due to physical constraints, embedded computers and networks may give rise to some additional security vulnerabilities, which results in losses of enormous economy benefits or disorder of social life. As a result, it is of significant to properly investigate the security issue of CPSs to ensure that such systems are operating in a safe manner. This paper, from a control theory perspective, presents an overview of recent advances on security control and attack detection of industrial CPSs. First, the typical system modeling on CPSs is summarized to cater for the requirement of the performance analysis. Then three typical types of cyber-attacks, i.e. denial-of-service attacks, replay attacks, and deception attacks, are disclosed from an engineering perspective. Moreover, robustness, security and resilience as well as stability are discussed to govern the capability of weakening various attacks. The development on attack detection for industrial CPSs is reviewed according to the categories on detection approaches. Furthermore, the security control and state estimation are discussed in detail. Finally, some challenge issues are raised for the future research.
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