Abstract
Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, Big Data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. To improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods including machine learning, Big Data techniques and deployed in a cloud environment. We use the industrial dataset SWat for the experiment. Our system achieved good results in terms of Accuracy (99%) and response time by using Gradient-Boosted Trees (GBTs) classifier due to the performance of Databricks and Apache Spark.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Industry 4.0 challenges and solutions for the digital transformation and use of exponential technologies. Finance, Audit Tax Consulting Corporate, Zurich, Swiss (2015)
Apache spark: Evaluation metrics. https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html Accessed 25 Oct 2021
Databricks architecture overview. https://docs.databricks.com/getting-started/overview.html Accessed 25 Oct 2021
Executive office of the president of the united states: office of science and technology policy. Summary of the 2018 White House Summit on Artificial Intelligence for American Industry Product of the White House Office of Science And Technology Policy (2018)
Abid, A., Jemili, F.: Intrusion detection based on graph oriented big data analytics. Procedia Comput. Sci. 176, 572–581 (2020). Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information Engineering Systems, KES 2020
Alhaidari, F.A., AL-Dahasi, E.M.: New approach to determine DDoS attack patterns on SCADA system using machine learning. In: 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1–6 (2019). https://doi.org/10.1109/ICCISci.2019.8716432
Channe, C.: Artificial intelligence in cyber security market - global trends, market share, industry size, growth, opportunities, and market in us forecast, 2019–2025. Industry Daily Observer (2019)
Choi, Seungoh, Yun, Jeong-Han., Kim, Sin-Kyu.: A comparison of ICS datasets for security research based on attack paths. In: Luiijf, Eric, Žutautaitė, Inga, Hämmerli, Bernhard M.. (eds.) CRITIS 2018. LNCS, vol. 11260, pp. 154–166. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05849-4_12
Goh, Jonathan, Adepu, Sridhar, Junejo, Khurum Nazir, Mathur, Aditya: A dataset to support research in the design of secure water treatment systems. In: Havarneanu, Grigore, Setola, Roberto, Nassopoulos, Hypatia, Wolthusen, Stephen (eds.) CRITIS 2016. LNCS, vol. 10242, pp. 88–99. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71368-7_8
Inoue, J., Yamagata, Y., Chen, Y., Poskitt, C., Sun, J.: Anomaly detection for a water treatment system using unsupervised machine learning. In: 17th IEEE International Conference on Data Mining Workshops ICDMW, 18–21 Nov 2017, New Orleans, pp. 1058–1065 (2017). https://doi.org/10.1109/ICDMW.2017.149
Khan, A.A.Z., Serpen, G.: Misuse intrusion detection using machine learning for gas pipeline SCADA networks. In: International Conference on Security and Management (SAM), Las Vegas, NV, USA (2019)
Kravchik, M., Shabtai, A.: Detecting cyber attacks in industrial control systems using convolutional neural networks. In: Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and Privacy, CPS-SPC 2018, pp. 72–83. Association for Computing Machinery, New York, USA (2018). https://doi.org/10.1145/3264888.3264896, https://doi.org/10.1145/3264888.3264896
Lezzi, M., Lazoi, M., Corallo, A.: Cybersecurity for industry 4.0 in the current literature: a reference framework. Comput. Ind. 103, 97–110 (2018). https://doi.org/10.1016/j.compind.2018.09.004. www.sciencedirect.com/science/article/pii/S0166361518303658
Morris, Thomas, Gao, Wei: Industrial control system traffic data sets for intrusion detection research. In: Butts, Jonathan, Shenoi, Sujeet (eds.) ICCIP 2014. IAICT, vol. 441, pp. 65–78. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45355-1_5
Perales Gomez, L., Fernandez Maimo, L., Huertas Celdran, A., Garcia Clemente, F.J.: MADICS: a methodology for anomaly detection in industrial control systems. Symmetry 12(10), 1583 (2020). https://doi.org/10.3390/sym12101583. https://www.mdpi.com/2073-8994/12/10/1583
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Abid, A., Jemili, F., Korbaa, O. (2022). Distributed Architecture of an Intrusion Detection System in Industrial Control Systems. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-031-16210-7_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16209-1
Online ISBN: 978-3-031-16210-7
eBook Packages: Computer ScienceComputer Science (R0)