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Standards, Governance and Policy. Cybersecurity of the Internet of Things (IoT): PETRAS Stream Report

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... Apart from recovery planning, other challenges found in literature for SME's integration in Industry 4.0 supply chains are: a) robustness, safety, and security (Akinrolabu et al. 2019;Brass et al. 2018;Brass et al. 2019;Hahn et al. 2013;Nicolescu et al. 2018a;Zhu et al. 2011); b) control and hybrid systems (Agyepong et al. 2019;Leitão et al. 2016;Nurse et al. 2018;Shi et al. 2011); c) computational abstractions (Ani et al. 2019;Madakam et al. 2015;Radanliev et al. 2018b;Rajkumar et al. 2010;Wahlster et al. 2013); d) real-time embedded systems abstractions (Ghirardello et al. 2018;Kang et al. 2012;Leitão et al. 2016;Marwedel and Engel 2016;PETRAS 2020;Shi et al. 2011;Tan et al. 2008); e) model-based development (Bhave et al. 2011;Jensen et al. 2011;Rajkumar et al. 2010;Shi et al. 2011;Taylor et al. 2018;Wahlster et al. 2013); and f) education and training (Faller and Feldmüller 2015;Nicolescu et al. 2018b;Petar Radanliev et al. 2020;Rajkumar et al. 2010;Wahlster et al. 2013). ...
Article
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Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.
... Apart from recovery planning, other challenges found in literature for SME's integration in Industry 4.0 supply chains are: 20 a) robustness, safety, and security (Akinrolabu et al., 2019;Irina Brass, Pothong, Tanczer, & Carr, 2019;Hahn, Ashok, Sridhar, & Govindarasu, 2013;Nicolescu, Huth, Radanliev, & De Roure, 2018a;Zhu et al., 2011); b) control and hybrid systems (Agyepong et al., 2019;Leitão et al., 2016;J. R. Nurse, Radanliev, Creese, & De Roure, 2018;Shi et al., 2011); c) computational abstractions (Ani, Watson, Nurse, Cook, & Maple, 2019;Madakam, Ramaswamy, & Tripathi, 2015;Rajkumar et al., 2010;Wahlster et al., 2013); d) real-time embedded systems abstractions (Ghirardello et al., 2018;Kang et al., 2012;Leitão et al., 2016;Marwedel & Engel, 2016;PETRAS, 2020;Shi et al., 2011;Tan et al., 2008); e) model-based development (Bhave, Krogh, Garlan, & Schmerl, 2011;Jensen et al., 2011;Rajkumar et al., 2010;Shi et al., 2011;Taylor, P., Allpress, S., Carr, M., Lupu, E., Norton, J., Smith et al., 2018;Wahlster et al., 2013); and f) education and training (Faller & Feldmüller, 2015;Nicolescu, Huth, Radanliev, & De Roure, 2018b;Rajkumar et al., 2010;Wahlster et al., 2013). ...
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Full-text available
Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.
... Apart from recovery planning, other challenges found in literature for SME's integration in Industry 4.0 supply chains are: 20 a) robustness, safety, and security (Akinrolabu et al., 2019;Irina Brass, Pothong, Tanczer, & Carr, 2019;Hahn, Ashok, Sridhar, & Govindarasu, 2013;Nicolescu, Huth, Radanliev, & De Roure, 2018a;Zhu et al., 2011); b) control and hybrid systems (Agyepong et al., 2019;Leitão et al., 2016;J. R. Nurse, Radanliev, Creese, & De Roure, 2018;Shi et al., 2011); c) computational abstractions (Ani, Watson, Nurse, Cook, & Maple, 2019;Madakam, Ramaswamy, & Tripathi, 2015;Rajkumar et al., 2010;Wahlster et al., 2013); d) real-time embedded systems abstractions (Ghirardello et al., 2018;Kang et al., 2012;Leitão et al., 2016;Marwedel & Engel, 2016;PETRAS, 2020;Shi et al., 2011;Tan et al., 2008); e) model-based development (Bhave, Krogh, Garlan, & Schmerl, 2011;Jensen et al., 2011;Rajkumar et al., 2010;Shi et al., 2011;Taylor, P., Allpress, S., Carr, M., Lupu, E., Norton, J., Smith et al., 2018;Wahlster et al., 2013); and f) education and training (Faller & Feldmüller, 2015;Nicolescu, Huth, Radanliev, & De Roure, 2018b;Rajkumar et al., 2010;Wahlster et al., 2013). ...
... Apart from recovery planning, other challenges found in literature for SME's integration in Industry 4.0 supply chains are: a) robustness, safety, and security (Akinrolabu et al. 2019;Brass et al. 2018;Brass et al. 2019;Hahn et al. 2013;Nicolescu et al. 2018a;Zhu et al. 2011); b) control and hybrid systems (Agyepong et al. 2019;Leitão et al. 2016;Nurse et al. 2018;Shi et al. 2011); c) computational abstractions (Ani et al. 2019;Madakam et al. 2015;Radanliev et al. 2018b;Rajkumar et al. 2010;Wahlster et al. 2013); d) real-time embedded systems abstractions (Ghirardello et al. 2018;Kang et al. 2012;Leitão et al. 2016;Marwedel and Engel 2016;PETRAS 2020;Shi et al. 2011;Tan et al. 2008); e) model-based development (Bhave et al. 2011;Jensen et al. 2011;Rajkumar et al. 2010;Shi et al. 2011;Taylor et al. 2018;Wahlster et al. 2013); and f) education and training (Faller and Feldmüller 2015;Nicolescu et al. 2018b;Petar Radanliev et al. 2020;Rajkumar et al. 2010;Wahlster et al. 2013). ...
Preprint
Full-text available
Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.
... Apart from recovery planning, other challenges found in literature for SME's integration in Industry 4.0 supply chains are: 20 a) robustness, safety, and security (Akinrolabu et al., 2019;Irina Brass, Pothong, Tanczer, & Carr, 2019;Hahn, Ashok, Sridhar, & Govindarasu, 2013;Nicolescu, Huth, Radanliev, & De Roure, 2018a;Zhu et al., 2011); b) control and hybrid systems (Agyepong et al., 2019;Leitão et al., 2016;J. R. Nurse, Radanliev, Creese, & De Roure, 2018;Shi et al., 2011); c) computational abstractions (Ani, Watson, Nurse, Cook, & Maple, 2019;Madakam, Ramaswamy, & Tripathi, 2015;Rajkumar et al., 2010;Wahlster et al., 2013); d) real-time embedded systems abstractions (Ghirardello et al., 2018;Kang et al., 2012;Leitão et al., 2016;Marwedel & Engel, 2016;PETRAS, 2020;Shi et al., 2011;Tan et al., 2008); e) model-based development (Bhave, Krogh, Garlan, & Schmerl, 2011;Jensen et al., 2011;Rajkumar et al., 2010;Shi et al., 2011;Taylor, P., Allpress, S., Carr, M., Lupu, E., Norton, J., Smith et al., 2018;Wahlster et al., 2013); and f) education and training (Faller & Feldmüller, 2015;Nicolescu, Huth, Radanliev, & De Roure, 2018b;Rajkumar et al., 2010;Wahlster et al., 2013). ...
Preprint
Full-text available
Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.
Chapter
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This report was based on a workshop. The impetus for this workshop was the recognition that international policy cooperation on the cybersecurity aspects of the IoT has made little progress. This is due in part to a failure to establish a functioning community of technicians and policymakers who are jointly focusing on these issues. From a technical perspective, the IoT will significantly increase opportunities to breach security via new attack surfaces. For policymakers, the heightened insecurity created by the rapid expansion of the IoT marks a significant governance challenge. Addressing these security deficiencies will require an increase in the capacity to share threat information as well as a range of innovative technical and policy solutions. The workshop marked a starting point in building a global community of security practitioners and policymakers who are interested in these issues and who are working on similar topics.
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