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Articles

Distributed deep learning approach for intrusion detection system in industrial control systems based on big data technique and transfer learning

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Pages 513-541 | Received 31 Dec 2022, Accepted 12 Jul 2023, Published online: 25 Jul 2023

References

  • Abedin, R., & Waheed, S. (2022, December). Performance analysis of machine learning models for intrusion detection system using gini impurity-based weighted random forest (giwrf) feature selection technique. Cybersecurity, 5. https://doi.org/10.1186/s42400-021-00103-8.
  • Abid, A., & Jemili, F. (2020). Intrusion detection based on graph oriented big data analytics. Procedia Computer Science, 176, 572–581. https://doi.org/10.1016/j.procs.2020.08.059Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020.
  • Abid, A., Jemili, F., & Korbaa, O. (2022). Distributed architecture of an intrusion detection system in industrial control systems. In C. Bădică, J. Treur, D. Benslimane, B. Hnatkowska, & M. Krótkiewicz (Eds.), Advances in computational collective intelligence (pp. 472–484). Springer International Publishing.
  • Abid, A., Jemili, F., & Korbaa, O. (2023, June). Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques. Cluster Computing, 1–22. https://doi.org/10.1007/s10586-023-04087-7.
  • Alhaidari, F. A., & AL-Dahasi, E. M. (2019). 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). https://doi.org/10.1109/ICCISci.2019.8716432.
  • Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A. Q., Duan, Y., Al-Shamma, O., Santamará, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 1–74. https://doi.org/10.1186/S40537-021-00444-8.
  • Apache Spark (2021). Evaluation metrics. Retrieved October 25, 2021, from https://spark.apache.org/docs/latest/mllib-evaluation-metrics.html.
  • Channe, C. (2019, May). Artificial intelligence in cyber security market–global trends, market share, industry size, growth, opportunities, and market in us forecast, 2019–2025. Industry Daily Observer.
  • Choi, S., Yun, J. H., & Kim, S. K. (2019). A comparison of ics datasets for security research based on attack paths. In E. Luiijf, I. Žutautaitė, & B. M. Hämmerli (Eds.), Critical information infrastructures security (pp. 154–166). Springer International Publishing.
  • Classification and Pegression (2022). Retrieved December 5, 2022, from https://spark.apache.org/docs/latest/ml-classification-regression.html.
  • Databricks architecture overview (2021). Retrieved October 25, 2021, from https://docs.databricks.com/getting-started/overview.html.
  • Deloitte (2015). Industry 4.0 challenges and solutions for the digital transformation and use of exponential technologies. In Finance, audit tax consulting corporate: Zurich, swiss.
  • Deng, L., & Yu, D. (2014, June). Deep learning: Methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197–387. https://doi.org/10.1561/2000000039.
  • Elnour, M., Meskin, N., Khan, K., & Jain, R. (2020). A dual-isolation-forests-based attack detection framework for industrial control systems. IEEE Access, 8, 36639–36651. https://doi.org/10.1109/ACCESS.2020.2975066.
  • Executive office of the president of the united states (2018, May). 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.
  • Goh, J., Adepu, S., Junejo, K. N., & Mathur, A. (2017). A dataset to support research in the design of secure water treatment systems. In G. Havarneanu, R. Setola, H. Nassopoulos, & S. Wolthusen (Eds.), Critical information infrastructures security (pp. 88–99). Springer International Publishing.
  • Gu, J., & Lu, S. (2021). An effective intrusion detection approach using svm with naïve bayes feature embedding. Computers & Security, 103, 102158. https://doi.org/10.1016/j.cose.2020.102158.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
  • Inoue, J., Yamagata, Y., Chen, Y., Poskitt, C., & Sun, J. (2017, November 18–21). Anomaly detection for a water treatment system using unsupervised machine learning. In 17th IEEE international conference on data mining workshops ICDMW (pp. 1058–1065). https://doi.org/10.1109/ICDMW.2017.149.
  • Jemili, F. (2023). Towards data fusion-based big data analytics for intrusion detection. Journal of Information and Telecommunication, 0(0), 1–28. https://doi.org/10.1080/24751839.2023.2214976.
  • Khan, A. A. Z., & Serpen, G. (2019). Misuse intrusion detection using machine learning for gas pipeline scada networks. In International conference on security and management (SAM).
  • Kim, J., Yun, J. H., & Kim, H. C. (2020). Anomaly detection for industrial control systems using sequence-to-sequence neural networks. In S. Katsikas, F. Cuppens, N. Cuppens, C. Lambrinoudakis, C. Kalloniatis, J. Mylopoulos, A. Antón, S. Gritzalis, F. Pallas, J. Pohle, A. Sasse, W. Meng, S. Furnell, & J. Garcia-Alfaro (Eds.), Computer security (pp. 3–18). Springer International Publishing.
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Kravchik, M., & Shabtai, A. (2018). 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 '18 (pp. 72–83). Association for Computing Machinery. https://doi.org/10.1145/3264888.3264896.
  • Lai, Y., Zhang, J., & Liu, Z. (2019). Industrial anomaly detection and attack classification method based on convolutional neural network. Security and Communication Networks, 2019, September, 1–11. https://doi.org/10.1155/2019/8124254.
  • Lezzi, M., Lazoi, M., & Corallo, A. (2018). Cybersecurity for industry 4.0 in the current literature: A reference framework. Computers in Industry, 103, 97–110. https://doi.org/10.1016/j.compind.2018.09.004.
  • Morris, T., & Gao, W. (2014). Industrial control system traffic data sets for intrusion detection research. In J. Butts & S. Shenoi (Eds.), Critical infrastructure protection VIII (pp. 65–78). Springer Berlin Heidelberg.
  • Moustafa, N. (2021). A new distributed architecture for evaluating ai-based security systems at the edge: Network ton_iot datasets. Sustainable Cities and Society, 72, 102994. https://doi.org/10.1016/j.scs.2021.102994.
  • Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019). Speech recognition using deep neural networks: A systematic review. IEEE Access, 7, 19143–19165. https://doi.org/10.1109/ACCESS.2019.2896880.
  • Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2021, March). Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2). https://doi.org/10.1145/3439950.
  • Perales Gomez, L., Fernandez Maimo, L., Huertas Celdran, A., & F. J. Garcia Clemente (2020). Madics: A methodology for anomaly detection in industrial control systems. Symmetry, 12(10). https://doi.org/10.3390/sym12101583.
  • Rajkumar, P. V., Ghosh, S. K., & Dasgupta, P. (2010). Concurrent usage control implementation verification using the spin model checker. In N. Meghanathan, S. Boumerdassi, N. Chaki, & D. Nagamalai (eds.), Recent trends in network security and applications (pp. 214–223). Springer Berlin Heidelberg.
  • Rajkumar, P. V., & Sandhu, R. (2016a). Poster: Security enhanced administrative role based access control models. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, CCS '16 (pp. 1802–1804). Association for Computing Machinery. https://doi.org/10.1145/2976749.2989068.
  • Rajkumar, P. V., & Sandhu, R. (2016b). Safety decidability for pre-authorization usage control with finite attribute domains. IEEE Transactions on Dependable and Secure Computing, 13(5), 582–590. https://doi.org/10.1109/TDSC.2015.2427834.
  • Rajkumar, P. V., & Sandhu, R. (2020). Safety decidability for pre-authorization usage control with identifier attribute domains. IEEE Transactions on Dependable and Secure Computing, 17(3), 465–478. https://doi.org/10.1109/TDSC.2018.2839745.
  • Sarkar, A., Sharma, H., & Singh, M. (2022, October). A supervised machine learning-based solution for efficient network intrusion detection using ensemble learning based on hyperparameter optimization. International Journal of Information Technology, 15. https://doi.org/10.1007/s41870-022-01115-4.
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003.
  • Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N., & Fox, E. A. (2020). Natural language processing advancements by deep learning: A survey. https://doi.org/10.48550/ARXIV.2003.01200.
  • Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018, February, 1–13. https://doi.org/10.1155/2018/7068349.
  • Wang, W., Wang, Z., Zhou, Z., Deng, H., Zhao, W., Wang, C., & Guo, Y. (2021). Anomaly detection of industrial control systems based on transfer learning. Tsinghua Science and Technology, 26(6), 821–832. https://doi.org/10.26599/TST.2020.9010041.
  • Xiang, S., Nie, F., & Zhang, C. (2008, December). Learning a mahalanobis distance metric for data clustering and classification. Pattern Recognition, 41(12), 3600–3612. https://doi.org/10.1016/j.patcog.2008.05.018.
  • Zhang, Z. (2018). Improved adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS) (pp. 1–2). https://doi.org/10.1109/IWQoS.2018.8624183.