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Abstract

In the context of factory digitization, the focus of Industry 4.0 initiative is to leverage the automation and data exchange in manufacturing processes using automation, IoT, and advanced technologies. Industry 5.0 has emerged as the next industrial revolution, to adopt a human-centric approach for digital technologies. That said, having people, robots, smart machines, and intelligent production systems working together raises challenges in terms of monitoring and securing the production environment. In other words, the complexity of smart factories makes it possible for incidents to occur and interrupt the manufacturing process. They might also cause leaking sensitive information or putting the worker’s life in danger as well. Accordingly, designing secure architectures in intelligent factories is crucial to secure a continuous production lifecycle and safe working conditions. Nonetheless, due to the decentralized environment of modern factories, creating multi-domain data correlation is crucial to obtain universal monitoring of the occurring events, as well as to establish cyber situational awareness and adopt suitable countermeasures in case of attacks. For these purposes, we developed a holistic multi-domain security and safety mechanism for Factories of the Future. Our approach consists of individual but integrated modules, each of which is responsible for capturing alerts from different domains of the factory. Alerts are then handled by an intelligent correlator to inspect possible attacks or malfunctions on the investigated shop floor. In this paper, we describe the architecture and design of the developed approach.

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References

  1. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014). https://doi.org/10.1007/s12599-014-0334-4

    Article  Google Scholar 

  2. Industry 5.0 | European Commission: https://ec.europa.eu/info/research-and-innovation/research-area/industrial-research-and-innovation/industry-50_en. Last accessed 26 April 2022

  3. Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L.: Industry 4.0 and industry 5.0—inception, conception and perception. J. Manuf. Syst. 61, 530–535 (2021)

    Google Scholar 

  4. Breque, M., De Nul, L., Petridis, A.: Industry 5.0: towards a sustainable, human-centric and resilient European industry. Luxemb. LU Eur. Comm. Dir. Res. Innov. (2021)

    Google Scholar 

  5. Becue, A., Maia, E., Feeken, L., Borchers, P., Praca, I.: A new concept of digital twin supporting optimization and resilience of factories of the future. Appl. Sci. 10, 4482 (2020)

    Article  Google Scholar 

  6. Luo, R.C., Lin, S.Y., Su, K.L.: A multiagent multisensor based security system for intelligent building. In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI2003, pp. 311–316 (2003)

    Google Scholar 

  7. de Silva, M.J., Pereira, C.E., Götz, M.: Context-aware recommendation for industrial alarm system. IFAC-PapersOnLine. 51, 229–234 (2018)

    Article  Google Scholar 

  8. Park, S.-T., Li, G., Hong, J.-C.: A study on smart factory-based ambient intelligence context-aware intrusion detection system using machine learning. J. Ambient. Intell. Humaniz. Comput. 11(4), 1405–1412 (2018). https://doi.org/10.1007/s12652-018-0998-6

    Article  Google Scholar 

  9. Moustafa, N., Adi, E., Turnbull, B., Hu, J.: A new threat intelligence scheme for safeguarding industry 4.0 systems. IEEE Access. 6, 32910–32924 (2018)

    Google Scholar 

  10. Nwakanma, C.I., Islam, F.B., Maharani, M.P., Lee, J.-M., Kim, D.-S.: Detection and classification of human activity for emergency response in smart factory shop floor. Appl. Sci. 11, 3662 (2021)

    Article  Google Scholar 

  11. Settanni, G., Shovgenya, Y., Skopik, F., Graf, R., Wurzenberger, M., Fiedler, R.: Acquiring cyber threat intelligence through security information correlation. In: 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1–7 (2017)

    Google Scholar 

  12. Settanni, G., Skopik, F., Shovgenya, Y., Fiedler, R.: A collaborative analysis system for cross-organization cyber incident handling. In: ICISSP, pp. 105–116 (2016)

    Google Scholar 

  13. Leszczyna Rafałand Wróbel, M.: Threat intelligence platform for the energy sector. Softw. Pract. Exp. 49, 1225–1254 (2019)

    Google Scholar 

  14. Marchetti, M., Pierazzi, F., Guido, A., Colajanni, M.: Countering advanced persistent threats through security intelligence and big data analytics. In: 2016 8th International Conference on Cyber Conflict (CyCon), pp. 243–261 (2016)

    Google Scholar 

  15. Dron, W., et al.: Multi-domain integration and correlation engine. In: MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), pp. 1–9 (2018)

    Google Scholar 

  16. Castro, A., Fuentes, B., Lozano, J.A., Costales, B., Villagrá, V.: Multi-domain fault management architecture based on a shared ontology-based knowledge plane. In: 2010 International Conference on Network and Service Management, pp. 493–498 (2010)

    Google Scholar 

  17. Wang, J., Zhang, Y., Duan, L., Wang, X.: Multi-domain sequential signature analysis for machinery intelligent diagnosis. In: 2016 10th International Conference on Sensing Technology (ICST), pp. 1–6 (2016)

    Google Scholar 

  18. The C4 model for visualising software architecture. https://c4model.com/. Last accessed 29 April 2022

  19. Apache Kafka: https://kafka.apache.org/. Last accessed 29 April 2022

  20. Dias, T., Oliveira, N., Sousa, N., Praça, I., Sousa, O.: A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System. In: Abraham, A., Gandhi, N., Hanne, T., Hong, T.-P., Nogueira Rios, T., Ding, W. (eds.) ISDA 2021. LNNS, vol. 418, pp. 1035–1045. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96308-8_96

    Chapter  Google Scholar 

  21. Silva, F., Santos, G., Praça, I., Vale, Z.: A context-based building security alarm through power and sensors analysis. Energy Informatics 1(1), 349–353 (2018). https://doi.org/10.1186/s42162-018-0045-z

    Article  Google Scholar 

  22. Wannous, S., Praça, I., Andrade, R.: Intelligence as a Service: A Tool for Energy Forecasting and Security Awareness. In: De La Prieta, F., El Bolock, A., Durães, D., Carneiro, J., Lopes, F., Julian, V. (eds.) PAAMS 2021. CCIS, vol. 1472, pp. 176–186. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85710-3_15

    Chapter  Google Scholar 

  23. Gu, S., Wang, F., Patel, N.P., Bourgeois, J.A., Huang, J.H.: A model for basic emotions using observations of behavior in Drosophila. Front. Psychol. 781 (2019)

    Google Scholar 

  24. Home – Suricata: https://suricata.io/. Last accessed 28 April 2022

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Acknowledgements

The present work has been developed under the EUREKA ITEA3 Project Cyber-Factory#1 (ITEA-17032) and Project CyberFactory#1PT (ANI—P2020 40124) co-funded by Portugal 2020. Furthermore, this work has also received funding from the project UIDB/00760/2020.

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Correspondence to Sinan Wannous .

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Wannous, S., Dias, T., Maia, E., Praça, I., Faria, A.R. (2022). Multiple Domain Security Awareness for Factories of the Future. In: González-Briones, A., et al. Highlights in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Communications in Computer and Information Science, vol 1678. Springer, Cham. https://doi.org/10.1007/978-3-031-18697-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-18697-4_3

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