Engineering of Next Generation Cyber-Physical Automation System Architectures

Foehr, Matthias; Vollmar, Jan; Calà, Ambra; Leitão, Paulo; Karnouskos, Stamatis; Colombo, Armando Walter

doi:10.1007/978-3-319-56345-9_8

Cited by 22 publications

(16 citation statements)

References 26 publications

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“…Hence, consistent communication is essential for successful order management. Furthermore, the host computer and legacy systems are highly customized for specific responsibilities; which impedes modifications [2].…”

Section: Classic Production Control Architectures (Cpca)mentioning

confidence: 99%

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Comparative study of flexible and decentralized agent-based and service-oriented control architectures for production systems

Rothkugel

Frank

Tanja

et al. 2018

2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)

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The purpose of this paper is the presentation and comparison of control architectures for the modular factory. In the last few years, service-oriented architectures and agent systems evolved to the most suitable technologies for flexible and versatile production systems which cope with challenges resulting from shortened product life cycles and product customization. However, decentralization as a rising concept to reduce complexity in production systems is often ignored. Therefore, this paper introduces service-oriented and agentbased control architectures which are based on the Production Priority Plan guaranteeing more flexibility and enabling decentralized decision-making in order to react to unexpected events. The architectures are compared in test scenarios derived from future production system challenges. The study demonstrates the great potential of the flexible, versatile and decentralized architectural concepts for production systems by contrast with today's production controls. Furthermore, applications more suitable for a concept due to its specific characteristics are highlighted.

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Section: Classic Production Control Architectures (Cpca)mentioning

confidence: 99%

“…Legacy production controls consisting of numerous subsystems are incapable of exploiting the maximum potential of CPPS because of their fragmented composition. Besides, centralized information storage in ERPs limits the optimization possibilities in production control and impedes flexible counteraction on unexpected events [2].…”

Section: Introductionmentioning

confidence: 99%

Comparative study of flexible and decentralized agent-based and service-oriented control architectures for production systems

Rothkugel

Frank

Tanja

et al. 2018

2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)

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“…Industrial systems such as oil and gas are generally divided into various functional layers depending upon their needs and focus. ISA-95 comprises of five hierarchical levels ranging from the business layer to the actual physical processes layer as shown in Figure 1 adapted from [9]. Figure 1 shows well-defined information processing within six functionalities.…”

Section: Introductionmentioning

confidence: 99%

Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures

et al. 2020

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Oil and Gas organizations are dependent on their IT infrastructure, which is a small part of their industrial automation infrastructure, to function effectively. The oil and gas (O&G) organizations industrial automation infrastructure landscape is complex. To perform focused and effective studies, Industrial systems infrastructure is divided into functional levels by The Instrumentation, Systems and Automation Society (ISA) Standard ANSI/ISA-95:2005. This research focuses on the ISA-95:2005 level-4 IT infrastructure to address network anomaly detection problem for ensuring the security and reliability of Oil and Gas resource planning, process planning and operations management. Anomaly detectors try to recognize patterns of anomalous behaviors from network traffic and their performance is heavily dependent on extraction time and quality of network traffic features or representations used to train the detector. Creating efficient representations from large volumes of network traffic to develop anomaly detection models is a time and resource intensive task. In this study we propose, implement and evaluate use of Deep learning to learn effective Network data representations from raw network traffic to develop data driven anomaly detection systems. Proposed methodology provides an automated and cost effective replacement of feature extraction which is otherwise a time and resource intensive task for developing data driven anomaly detectors. The ISCX-2012 dataset is used to represent ISA-95 level-4 network traffic because the O&G network traffic at this level is not much different than normal internet traffic. We trained four representation learning models using popular deep neural network architectures to extract deep representations from ISCX 2012 traffic flows. A total of sixty anomaly detectors were trained by authors using twelve conventional Machine Learning algorithms to compare the performance of aforementioned deep representations with that of a human-engineered handcrafted network data representation. The comparisons were performed using well known model evaluation parameters. Results showed that deep representations are a promising feature in engineering replacement to develop anomaly detection models for IT infrastructure security. In our future research, we intend to investigate the effectiveness of deep representations, extracted using ISA-95:2005 Level 2-3 traffic comprising of SCADA systems, for anomaly detection in critical O&G systems.

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“…At the same time, production organizations in critical and regulated domains, e.g., automotive, aerospace, and healthcare express an increasing interest in utilizing the DevOps approach for developing and maintaining consumer CPSs (e.g., wearables, virtual reality), as it enables them to shorten time-to-market and be more responsive to operational demands of customers and the market in general (Foehr et al, 2017;Stirbu & Mikkonen, 2010). However, adopting DevOps in industrial domains is extremely challenging due to the complexity of critical CPSs and the devastating costs associated with their downtime, as well as strict requirements demanded by regulatory authorities within those domains (Giaimo, Yin, Berger, & Crnkovic, 2016;Törngren & Sellgren, 2018, Morales, Yasar & Volkmann, 2018.…”

Section: Introductionmentioning

confidence: 99%

Towards a Secure DevOps Approach for Cyber-Physical Systems

Abrahamsson

Botterweck²,

Ghanbari

et al. 2020

International Journal of Systems and Software Security and Protection

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With the expansion of cyber-physical systems (CPSs) across critical and regulated industries, systems must be continuously updated to remain resilient. At the same time, they should be extremely secure and safe to operate and use. The DevOps approach caters to business demands of more speed and smartness in production, but it is extremely challenging to implement DevOps due to the complexity of critical CPSs and requirements from regulatory authorities. In this study, expert opinions from 33 European companies expose the gap in the current state of practice on DevOps-oriented continuous development and maintenance. The study contributes to research and practice by identifying a set of needs. Subsequently, the authors propose a novel approach called Secure DevOps and provide several avenues for further research and development in this area. The study shows that, because security is a cross-cutting property in complex CPSs, its proficient management requires system-wide competencies and capabilities across the CPSs development and operation.

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Engineering of Next Generation Cyber-Physical Automation System Architectures

Cited by 22 publications

References 26 publications

Comparative study of flexible and decentralized agent-based and service-oriented control architectures for production systems

Comparative study of flexible and decentralized agent-based and service-oriented control architectures for production systems

Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures

Towards a Secure DevOps Approach for Cyber-Physical Systems

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