Cybersecurity challenges in downstream steel production processes

  1. Ordieres-Meré, Joaquín 2
  2. Wolff, Andreas 3
  3. Pacios-Álvarez, Antonia 4
  4. Bello-García, Antonio 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Departamento de Ingeniería de Organización, Administración de Empresas y Estadística, Universidad Politécnica de Madrid
  3. 3 VDEh-Betriebsforschungsinstitut
    info

    VDEh-Betriebsforschungsinstitut

    Düsseldorf, Alemania

    ROR https://ror.org/01y07bp24

  4. 4 Departamento de Sistemas Aeroespaciales, Transporte Aéreo y Aeropuertos, Universidad Politécnica de Madrid
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Revista:
IFAC-PapersOnLine

ISSN: 2405-8963

Año de publicación: 2022

Volumen: 55

Número: 40

Páginas: 283-288

Tipo: Artículo

DOI: 10.1016/J.IFACOL.2023.01.086 SCOPUS: 2-s2.0-85159347999 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: IFAC-PapersOnLine

Objetivos de desarrollo sostenible

Resumen

The goal of this paper is to explore proposals coming from different EU-RFCS research funded projects, in such a way that cybersecurity inside the steel industry can be increased from the Operational Technology area, with the current level of adopted Information Technology solutions. The dissemination project Control In Steel has reviewed different projects with different strategies, including ideas to be developed inside the Auto Surveillance project. An advanced control process strategy is considered and cloud based solutions are the main analysed alternatives. The different steps in the model lifecycle are considered where different cloud configurations provide different solutions. Advanced techniques such as UMAP projection are proposed to be used as detectors for anomalous behaviour in the continuous development / continuous implementation strategy, suitable for integration in processing workflows

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Información de financiación

The research described in the present paper has been developed within the project entitled “Dissemination and valorisation of RFCS-results in the field of “Advanced Automation and Control Solutions in Downstream Steel Processes (ControlInSteel)” (G.A. No 899208), and within the project ”Automatic surveillance of hot rolling area against intentional attacks and faults (AutoSurveillance)” (G.A. No 847202) which have been funded by Research Fund for Coal and Steel (RFCS) of the European Union (EU). The sole responsibility of the issues treated in the present paper lies with the authors; the Commission is not responsible for any use that may be made of the information contained therein. The authors wish to acknowledge with thanks the EU for the opportunity granted that has made possible the development of the present work. The authors also wish to thank all partners of the project for their support and the fruitful discussion that led to successful completion of the present work.

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Referencias bibliográficas

  • Alhayani, B., Abbas, S.T., Khutar, D.Z., and Mohammed, H.J. (2021). Best ways computation intelligent of face cyber attacks. Materials Today: Proceedings.
  • Badii, C., Bellini, P., Cenni, D., Mitolo, N., Nesi, P., Pantaleo, G., and Soderi, M. (2020). Industry 4.0 syn-optics controlled by iot applications in node-red. In 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Commu-nications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cy-bermatics), 54–61. IEEE.
  • Becht, E., McInnes, L., Healy, J., Dutertre, C.A., Kwok, I.W., Ng, L.G., Ginhoux, F., and Newell, E.W. (2019). Dimensionality reduction for visualizing single-cell data using umap. Nature biotechnology, 37(1), 38–44.
  • Branca, T.A., Fornai, B., Colla, V., Murri, M.M., Streppa, E., and Schröder, A.J. (2020). The challenge of digital-ization in the steel sector. Metals, 10(2), 288.
  • Chang, C.C., Yang, S.R., Yeh, E.H., Lin, P., and Jeng, J.Y. (2017). A kubernetes-based monitoring platform for dynamic cloud resource provisioning. In GLOBECOM 2017-2017 IEEE Global Communications Conference, 1–6. IEEE.
  • Draxler, M., Schenk, J., Bürgler, T., and Sormann, A. (2020). The steel industry in the european union on the crossroad to carbon lean production—status, initia-tives and challenges. BHM Berg-und Hüttenmännische Monatshefte, 165(5), 221–226.
  • Gaikwad, P., Mandal, A., Ruth, P., Juve, G., Król, D., and Deelman, E. (2016). Anomaly detection for scientific workflow applications on networked clouds. In 2016 International Conference on High Perfor-mance Computing Simulation (HPCS), 645–652. doi: 10.1109/HPCSim.2016.7568396.
  • Goldin, E., Feldman, D., Georgoulas, G., Castano, M., and Nikolakopoulos, G. (2017). Cloud computing for big data analytics in the process control industry. In 2017 25th Mediterranean Conference on Control and Automation (MED), 1373–1378. IEEE.
  • Goshime, Y., Kitaw, D., and Jilcha, K. (2018). Lean manufacturing as a vehicle for improving productivity and customer satisfaction: A literature review on metals and engineering industries. International Journal of Lean Six Sigma.
  • Hallin, A., Lindell, E., Jonsson, B., and Uhlin, A. (2022). Digital transformation and power relations. interpre-tative repertoires of digitalization in the swedish steel industry. Scandinavian Journal of Management, 38(1), 101183.
  • Hansen, E.B. and Bøgh, S. (2021). Artificial intelligence and internet of things in small and medium-sized enter-prises: A survey. Journal of Manufacturing Systems, 58, 362–372.
  • Hugo, ˚A., Morin, B., and Svantorp, K. (2020). Bridging mqtt and kafka to support c-its: A feasibility study. In 2020 21st IEEE International Conference on Mobile Data Management (MDM), 371–376. IEEE.
  • Ilin, I., Levina, A., Borremans, A., and Kalyazina, S. (2019). Enterprise architecture modeling in digital transformation era. In Energy Management of Munic-ipal Transportation Facilities and Transport, 124–142. Springer.
  • Isaksson, A.J., Harjunkoski, I., and Sand, G. (2018). The impact of digitalization on the future of control and operations. Computers & Chemical Engineering, 114, 122–129.
  • Jeon, B., Yoon, J.S., Um, J., and Suh, S.H. (2020). The architecture development of industry 4.0 compliant smart machine tool system (smts). Journal of Intelligent Manufacturing, 31(8), 1837–1859.
  • Kindratenko, V., Mu, D., Zhan, Y., Maloney, J., Hashemi, S.H., Rabe, B., Xu, K., Campbell, R., Peng, J., and Gropp, W. (2020). Hal: Com-puter system for scalable deep learning. In Prac-tice and Experience in Advanced Research Comput-ing, 41–48. ACM. doi:10.1145/3311790.3396649. URL https://doi.org/10.1145/3311790.3396649.
  • Li, W., Lemieux, Y., Gao, J., Zhao, Z., and Han, Y. (2019). Service mesh: Challenges, state of the art, and future research opportunities. In 2019 IEEE Interna-tional Conference on Service-Oriented System Engineer-ing (SOSE), 122–1225. IEEE.
  • Naqvi, S.N.Z., Yfantidou, S., and Zim´anyi, E. (2017). Time series databases and influxdb. Studienarbeit, Universit´e Libre de Bruxelles, 12.
  • Naranjo, D.M., Risco, S., Moltó, G., and Blanquer, I. (2021). A serverless gateway for event-driven machine learning inference in multiple clouds. Concurrency and Computation: Practice and Experience, e6728.
  • Nasar, M. and Kausar, M.A. (2019). Suitability of influxdb database for iot applications. International Journal of Innovative Technology and Exploring Engineering, 8(10), 1850–1857.
  • Núrk, J. (2019). Smart information system capabilities of digital supply chain business models. European Journal of Business Science and Technology, 5(2), 143–184.
  • Ordieres-Meré, J., Martínez-de Pisón-Ascacibar, F., González-Marcos, A., and Ortiz-Marcos, I. (2010). Comparison of models created for the prediction of the me-chanical properties of galvanized steel coils. Journal of Intelligent manufacturing, 21(4), 403–421.
  • Quader, M.A., Ahmed, S., Dawal, S., and Nukman, Y. (2016). Present needs, recent progress and future trends of energy-efficient ultra-low carbon dioxide (co2) steel-making (ulcos) program. Renewable and Sustainable Energy Reviews, 55, 537–549.
  • Rattanatamrong, P., Boonpalit, Y., Suwanjinda, S., Mang-meesap, A., Subraties, K., Daneshmand, V., Smallen, S., and Haga, J. (2020). Overhead study of telegraf as a real-time monitoring agent. In 2020 17th International Joint Conference on Computer Science and Software Engineering (JCSSE), 42–46. IEEE.
  • Rechberger, K., Spanlang, A., Sasiain Conde, A., Wolfmeir, H., and Harris, C. (2020). Green hydrogen-based direct reduction for low-carbon steelmaking. steel research international, 91(11), 2000110.
  • Rieger, J., Schenk, J., Buergler, T., Kofler, I., Schatzl, M., and Huemer, G. (2020). K1-met—a success story since almost 20 years. steel research international, 91(12), 2000233.
  • Sahai, Y. (2016). Tundish technology for casting clean steel: a review. Metallurgical and Materials Transactions B, 47(4), 2095–2106.
  • Souza Filho, I.R., Ma, Y., Kulse, M., Ponge, D., Gault, B., Springer, H., and Raabe, D. (2021). Sustainable steel through hydrogen plasma reduction of iron ore: Process, kinetics, microstructure, chemistry. Acta Materialia, 213, 116971.