Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0

Chhetri, Sujit Rokka; Faezi, Sina; Rashid, Nafiul; Faruque, Mohammad Abdullah Al

doi:10.1007/s41635-017-0031-0

Cited by 50 publications

(22 citation statements)

References 88 publications

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“…Various challenges arise for maintaining the security requirements for integrity, confidentiality, and availability by protecting each subsystem from unauthorized access, use, disclosure, disruption, improper modification or destruction of data [78]. More specifically, integrity is related with consistency, accuracy, and reliability of both the information data and the physical components of the entire supply chain and the product lifecycle.…”

Section: Security and Threat Detectionmentioning

confidence: 99%

“…Heretofore, the aforementioned issues have been highlighted in the literature [78], while several solutions, including cyber and physical detection methods, as well as standards (e.g., Reference Architecture Model Industry 4.0 (RAMI 4.0) [79], Industrial Internet Reference Architecture (IIRA) [80]) to safeguard manufacturing have been provided to realize an Industry 4.0 environment secure from known risks, vigilant against new threats, and resilient against zero-day attacks. Nevertheless, security issues in specific domains can drastically influence the operation of the entire system in a non-trivial and non-evident manner.…”

Section: Security and Threat Detectionmentioning

confidence: 99%

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Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

238

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Security and Threat Detectionmentioning

confidence: 99%

Section: Security and Threat Detectionmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

238

View full text Add to dashboard Cite

show abstract

“…This type of attack profoundly impacts data availability, as the information becomes inaccessible when needed for legitimate purposes. Various manufacturing and production facilities have been victims of ransomware attacks in recent years [37]. According to BlackFog, a cybersecurity consultancycorporation, the manufacturing business and public sector have been the most targeted industries hit by the ransomware attacks in the first and second quarter of 2020 [38].…”

Section: Information Security Challengesmentioning

confidence: 99%

“…Distributed Denial of Service (DDoS) attack is a type of malicious activity impacting the availability of internetfacing systems [37]. DDoS has become more prevalent with Industry 4.0 and smart manufacturing harnessing IIoT and cloud computing infrastructures.…”

Section: Information Security Challengesmentioning

confidence: 99%

Empowering ISA95 compliant traditional and smart manufacturing systems with the blockchain technology

et al. 2021

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Technological advancements in the information technology domain such as cloud computing, industrial internet of things (IIoT), machine to machine (M2M) communication, artificial intelligence (AI), etc. have started to profoundly impact and challenge not only the ISA95 compliant traditional (ISA95-CTS) but also the smart manufacturing systems (SMMS). Our literature survey pinpoints that systems scalability, interoperability, information security, and data quality domains are among those where many challenges occur. Blockchain technology (BCT) is a new breed of technology characterized by decentralized verifiability, transparency, data privacy, integrity, high availability, and data protection properties. Although many researchers leveraged BCT to empower various aspects of industrial manufacturing systems, there is no study dedicated to addressing the challenges impacting the manufacturing systems compliant with the ISA95 standard. Thereby, our study aims to fill the identified research gap systematically. This paper thoroughly analyzes the challenges hampering the ISA95-CTS and SMMS and methodically addresses them with corresponding BCT capabilities. Furthermore, this paper also discusses various aspects, including the weaknesses, of BCT convergence to ISA95-CTS and SMMS.

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“…Adversaries can launch single attacks to disrupt industrial activities in a closed-loop supply chain, or can implement large-scale attacks to cause disturbances across many critical services. For example, adversaries can attack inventory management stations and report false inventory information (e.g., low inventory) via spoofing [116], [139], [140]. The result will be inventory management falsely placing orders based on autonomous inventory management schemes.…”

Section: ) Attacks On Servicementioning

confidence: 99%

Secure Internet of Things (IoT)-Based Smart-World Critical Infrastructures: Survey, Case Study and Research Opportunities

et al. 2019

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The widespread adoption of the Internet of Things (IoT) technologies has drastically increased the breadth and depth of attack surfaces in networked systems, providing new mechanisms for the intrusion. In the context of smart-world critical infrastructures and cyber-physical systems, the rapid adoption of the IoT systems and infrastructures without thorough consideration for the risks and vulnerabilities has the potential for catastrophic damage to the privacy, safety, and security of individuals and corporations. While the IoT systems have the potential to increase productivity, accountability, traceability, and efficiency, their potential weaknesses are also more abundant. In this paper, we provide critical consideration of the security of the IoT systems as applied to smart-world critical infrastructures. Particularly, we carry out a detailed assessment of vulnerabilities in IoT-based critical infrastructures from the perspectives of applications, networking, operating systems, software, firmware, and hardware. In addition, we highlight the three key critical infrastructure IoT-based cyber-physical systems, namely the smart transportation, smart manufacturing, and smart grid. Moreover, we provide a broad collection of attack examples upon each of the key applications. Furthermore, we introduce a case study, in which we assess the impacts of potential attacks on critical IoT-based systems, using the smart transportation system as an example. Finally, we provide a set of best practices and address the necessary steps to enact countermeasures for any generic IoT-based critical infrastructure system. INDEX TERMS Cyber-physical systems, Internet of Things, security, critical infrastructure, case study, computing infrastructure. I. INTRODUCTION Advances in information communication technologies have given rise to the Internet of Things (IoT), which will play an increasingly important role in our daily lives [1]-[3]. In IoT, the massive number of deployed IoT devices (sensors, actuators, etc.) will be connected to collect data related to objects in critical infrastructures, including city and government, industrial manufacturing, energy, transportation, healthcare, and public safety infrastructures, among others, supporting numerous smart-world systems. The examples of such systems are smart manufacturing, smart cities, smart grid, smart transportation, smart home, and smart health systems, to name a few [4]-[11]. The associate editor coordinating the review of this manuscript and approving it for publication was Jinsong Wu.

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Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0

Cited by 50 publications

References 88 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Empowering ISA95 compliant traditional and smart manufacturing systems with the blockchain technology

Secure Internet of Things (IoT)-Based Smart-World Critical Infrastructures: Survey, Case Study and Research Opportunities

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