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A Snapshot Ensemble Deep Neural Network Model for Attack Detection in Industrial Internet of Things

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AI-Enabled Threat Detection and Security Analysis for Industrial IoT

Abstract

In the past few years, the Industrial Internet of Things (IIoT) started to emerge in different industry sectors. IIoT connects different physical equipment and systems in order to operate more efficiently. As the systems work simultaneously, the amount of data they produce and share increases resulting in more vulnerability against cyber and physical attacks. Since security is one of the major challenges in IIOT, this paper proposes a Machine Learning- based technique in order to detect cyber-attacks on IIoT systems., A Snapshot Ensemble Deep Neural Network (SEDNN) has been utilized and evaluated various metrics, including accuracy, precision, recall, and F1-score. The proposed model obtained an accuracy of 90.58% for cyber-attack detection. Also, precision, recall, F1-score are 87.42%, 93.77% and 90.48%, respectively.

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References

  1. E. Borgia, “The Internet of Things vision: Key features, applications and open issues,” Comput. Commun., vol. 54, pp. 1–31, 2014, https://doi.org/10.1016/j.comcom.2014.09.008.

    Article  Google Scholar 

  2. A. Alabasi, H. Karimipour, A. Dehghantanha, “An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System”, IEEE Access, vol. 8, pp. 83965-83973, April. 2020. doi: https://doi.org/10.1109/ACCESS.2020.2992249.

  3. J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications,” IEEE Internet Things J., vol. 4, no. 5, pp. 1125–1142, 2017, doi: https://doi.org/10.1109/JIOT.2017.2683200.

    Article  Google Scholar 

  4. V. V Potekhin, A. H. Bahrami, and B. Katalinič, “Developing manufacturing execution system with predictive analysis,” IOP Conf. Ser. Mater. Sci. Eng., vol. 966, p. 12117, 2020, doi: https://doi.org/10.1088/1757-899x/966/1/012117.

    Article  Google Scholar 

  5. J. Sakhnini, H. Karimipour, A. Dehghantanha, R. Parizi, G. Srivastava “Security Aspects of Internet of Things Aided Smart Grids: a Bibliometric Survey”, Internet of Things Journal—Elsevier, pp. 1–15, Sept. 2019. https://doi.org/10.1016/j.iot.2019.100111

  6. F. Anwaar, N. Iltaf, H. Afzal, and R. Nawaz, “HRS-CE: A hybrid framework to integrate content embeddings in recommender systems for cold start items,” J. Comput. Sci., vol. 29, pp. 9–18, 2018, doi: https://doi.org/10.1016/j.jocs.2018.09.008.

    Article  Google Scholar 

  7. S. M. Tahsien, H. Karimipour, P. Spachos, “Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey”, Journal of Network and Computer Applications—Elsevier, vol. 161, pp. 1–18, April. 2020. https://doi.org/10.1016/j.jnca.2020.102630

  8. A. Yazdinejad, R. M. Parizi, A. Dehghantanha, H. Karimipour, G. Srivastava, and M. Aledhari, “Enabling Drones in the Internet of Things with Decentralized Blockchain-based Security,” IEEE Internet of Things Journal, 2020.

    Google Scholar 

  9. E. Nowroozi, A. Dehghantanha, R. M. Parizi, and K.-K. R. Choo, “A survey of machine learning techniques in adversarial image forensics,” Computers & Security, vol. 100, p. 102092, 2021.

    Article  Google Scholar 

  10. A. Yazdinejad, G. Srivastava, R. M. Parizi, A. Dehghantanha, H. Karimipour, and S. R. Karizno, “SLPoW: Secure and Low Latency Proof of Work Protocol for Blockchain in Green IoT Networks,” in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020, pp. 1–5: IEEE.

    Google Scholar 

  11. M. Aledhari, R. M. Parizi, A. Dehghantanha and K. R. Choo, “A Hybrid RSA Algorithm in Support of IoT Greenhouse Applications,” 2019 IEEE International Conference on Industrial Internet (ICII), Orlando, FL, USA, 2019, pp. 233–240, https://doi.org/10.1109/ICII.2019.00049

  12. A. Yazdinejadna, R. M. Parizi, A. Dehghantanha, and M. S. Khan, “A kangaroo-based intrusion detection system on software-defined networks,” Computer Networks, vol. 184, p. 107688, 2021.

    Article  Google Scholar 

  13. A. Yazdinejad, R. M. Parizi, A. Dehghantanha, and K.-K. R. Choo, “Blockchain-enabled authentication handover with efficient privacy protection in SDN-based 5G networks,” IEEE Transactions on Network Science and Engineering, 2019.

    Google Scholar 

  14. M. Aledhari, R. Razzak, R. M. Parizi and F. Saeed, “Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications,” in IEEE Access, vol. 8, pp. 140699–140725, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3013541

  15. A. Yazdinejad, G. Srivastava, R. M. Parizi, A. Dehghantanha, K.-K. R. Choo, and M. Aledhari, “Decentralized authentication of distributed patients in hospital networks using blockchain,” IEEE journal of biomedical and health informatics, vol. 24, no. 8, pp. 2146–2156, 2020.

    Article  Google Scholar 

  16. H. M. Ruzbahani and H. Karimipour, “Optimal incentive-based demand response management of smart households,” in 2018 IEEE/IAS 54th Industrial and Commercial Power Systems Technical Conference (I&CPS), 2018, pp. 1–7, https://doi.org/10.1109/ICPS.2018.8369971.

  17. H. M. Ruzbahani, A. Rahimnejad, and H. Karimipour, “Smart Households Demand Response Management with Micro Grid,” in 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2019, pp. 1–5, https://doi.org/10.1109/ISGT.2019.8791595.

  18. E. Modiri, A. Azmoodeh, A. Dehghantanha, H. Karimipour, “Fuzzy Pattern Tree for Edge Malware Detection and Categorization in IoT”, Journal of Systems Architecture, vol. 9, pp. 1–7, Jan. 2018. https://doi.org/10.1016/j.sysarc.2019.01.017

  19. Z. Ling, J. Luo, Y. Xu, C. Gao, K. Wu, and X. Fu, “Security Vulnerabilities of Internet of Things: A Case Study of the Smart Plug System,” IEEE Internet Things J., vol. 4, no. 6, pp. 1899–1909, 2017, doi: https://doi.org/10.1109/JIOT.2017.2707465.

    Article  Google Scholar 

  20. M. Hazrati, H. Karimipour, A. N. Jahromi, A. Dehghantanha, “Ensemble sparse representation-based cyber threat hunting for security of smart cities”, Computer & Electrical Engineering Journal- Elsevier (IF: 2.6), Dec. 2020. https://doi.org/10.1016/j.compeleceng.2020.106825

  21. G. Apruzzese, M. Colajanni, L. Ferretti, A. Guido, and M. Marchetti, “On the effectiveness of machine and deep learning for cyber security,” in 2018 10th International Conference on Cyber Conflict (CyCon), 2018, pp. 371–390, https://doi.org/10.23919/CYCON.2018.8405026.

  22. J. Lee, J. Kim, I. Kim, and K. Han, “Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles,” IEEE Access, vol. 7, pp. 165607–165626, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2953095.

    Article  Google Scholar 

  23. A. Yazdinejad, H. HaddadPajouh, A. Dehghantanha, R. M. Parizi, G. Srivastava, and M.-Y. Chen, “Cryptocurrency malware hunting: A deep recurrent neural network approach,” Applied Soft Computing, vol. 96, p. 106630, 2020.

    Article  Google Scholar 

  24. A. Namavar Jahromi et al., “An improved two-hidden-layer extreme learning machine for malware hunting,” Computers & Security, vol. 89, p. 101655, 2020.

    Article  Google Scholar 

  25. A. Yazdinejad, R. M. Parizi, A. Dehghantanha, G. Srivastava, S. Mohan, and A. M. Rababah, “Cost optimization of secure routing with untrusted devices in software defined networking,” Journal of Parallel and distributed Computing, vol. 143, pp. 36–46, 2020

    Article  Google Scholar 

  26. J. Chen et al., “Collaborative Trust Blockchain Based Unbiased Control Transfer Mechanism for Industrial Automation,” IEEE Trans. Ind. Appl., vol. 56, no. 4, pp. 4478–4488, 2020, doi: https://doi.org/10.1109/TIA.2019.2959550.

    Article  Google Scholar 

  27. W. G. Hatcher and W. Yu, “A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends,” IEEE Access, vol. 6, pp. 24411–24432, 2018, doi: https://doi.org/10.1109/ACCESS.2018.2830661.

    Article  Google Scholar 

  28. M. Lavassani, S. Forsström, U. Jennehag, and T. Zhang, “Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT,” Sensors, vol. 18, p. 1532, May 2018, doi: https://doi.org/10.3390/s18051532.

  29. A. N. Jahromi, A. Dehghantanha, R. Choo, H. Karimipour, R. Parizi, “An Improved Two-Hidden-Layer Extreme Learning Machine for Malware Hunting”, Computer and Security, vol. 89, pp. 1–11, Sept. 2019. https://doi.org/10.1016/j.cose.2019.101655

  30. H. M. Rouzbahani, H. Karimipour, and L. Lei, “An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020, pp. 3637–3642, https://doi.org/10.1109/SMC42975.2020.9282837.

  31. F. Farahnakian and J. Heikkonen, “A deep auto-encoder based approach for intrusion detection system,” in 2018 20th International Conference on Advanced Communication Technology (ICACT), 2018, pp. 178–183, https://doi.org/10.23919/ICACT.2018.8323688.

  32. Mehdi Moukhafi, Khalid El Yassini, and Seddik Bri, “A novel hybrid GA and SVM with PSO feature selection for intrusion detection system,” I. J. Adv. Sci. Res. Eng. (ISSN 2454—8006), vol. 4, no. 5 SE-, pp. 129–134, May 2018, https://doi.org/10.31695/IJASRE.2018.32724.

  33. H. Mohammadi Rouzbahani, H. Karimipour, A. Rahimnejad, A. Dehghantanha, and G. Srivastava, “Anomaly Detection in Cyber-Physical Systems Using Machine Learning BT—Handbook of Big Data Privacy,” K.-K. R. Choo and A. Dehghantanha, Eds. Cham: Springer International Publishing, 2020, pp. 219–235.

    Google Scholar 

  34. R. Vijayanand, D. Devaraj, and B. Kannapiran, “A novel intrusion detection system for wireless mesh network with hybrid feature selection technique based on GA and MI,” J. Intell. Fuzzy Syst., vol. 34, pp. 1243–1250, 2018, doi: https://doi.org/10.3233/JIFS-169421.

    Article  Google Scholar 

  35. L. Khalvati, M. Keshtgary, and N. Rikhtegar, “Intrusion Detection based on a Novel Hybrid Learning Approach,” J. AI Data Min., vol. 6, no. 1, pp. 157–162, 2018, doi: https://doi.org/10.22044/jadm.2017.979.

    Article  Google Scholar 

  36. X. Li, M. Xu, P. Vijayakumar, N. Kumar, and X. Liu, “Detection of Low-Frequency and Multi-Stage Attacks in Industrial Internet of Things,” IEEE Trans. Veh. Technol., vol. 69, no. 8, pp. 8820–8831, 2020, doi: https://doi.org/10.1109/TVT.2020.2995133.

    Article  Google Scholar 

  37. P. Bereziński, B. Jasiul, and M. Szpyrka, “An Entropy-Based Network Anomaly Detection Method,” Entropy, vol. 17, no. 4, pp. 2367–2408, Apr. 2015, doi: https://doi.org/10.3390/e17042367.

  38. T. Morris and W. Gao, “Industrial Control System Traffic Data Sets for Intrusion Detection Research BT—Critical Infrastructure Protection VIII,” 2014, pp. 65–78.

    Google Scholar 

  39. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1–6, https://doi.org/10.1109/CISDA.2009.5356528.

  40. H. M. Rouzbahani, Z. Faraji, M. Amiri-Zarandi, and H. Karimipour, “AI-Enabled Security Monitoring in Smart Cyber Physical Grids”, Security of Cyber-Physical Systems: Vulnerability and Impact, Eds. Cham: Springer International Publishing, 2020, pp. 145–167.

    Google Scholar 

  41. M.-O. P. and F.-X. Aubet., “Ds2Os Traffic Traces IoT Traffic Traces Gathered in a The Ds2Os IoT Environment. [Online].,” 2018. https://www.kaggle.com/francoisxa/ds2ostraffictraces.

  42. M. Pahl and F. Aubet, “All Eyes on You: Distributed Multi-Dimensional IoT Microservice Anomaly Detection,” in 2018 14th International Conference on Network and Service Management (CNSM), 2018, pp. 72–80, [Online]. Available: http://doi.ieeecomputersociety.org/.

  43. M. Hasan, M. M. Islam, M. I. I. Zarif, and M. M. A. Hashem, “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches,” Internet of Things, vol. 7, p. 100059, 2019, doi: https://doi.org/10.1016/j.iot.2019.100059.

    Article  Google Scholar 

  44. O. Brun, Y. Yin, and E. Gelenbe, “Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments,” Procedia Comput. Sci., vol. 134, pp. 458–463, 2018, doi: https://doi.org/10.1016/j.procs.2018.07.183.

    Article  Google Scholar 

  45. F. Honghai, C. Guoshun, Y. Cheng, Y. Bingru, and C. Yumei, “A SVM Regression Based Approach to Filling in Missing Values BT—Knowledge-Based Intelligent Information and Engineering Systems,” 2005, pp. 581–587.

    Google Scholar 

  46. Sh. Yousefi, F. Derakhshan, H. Karimipour, “Applications of Big Data Analytics and Machine Learning in the Internet of Things”, Handbook of Big Data and Privacy, Springer Books, pp. 1–32, Feb. 2020. https://doi.org/10.1007/978-3-030-38557-6_5

  47. G. Huang, Y. Li, G. Pleiss, Z. Liu, J. Hopcroft, and K. Weinberger, “Snapshot Ensembles: Train 1, get M for free,” Mar. 2017.

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada

    Hossein Mohammadi Rouzbahani & Hadis Karimipour

  2. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Amir Hossein Bahrami

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  1. Hossein Mohammadi Rouzbahani
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  2. Amir Hossein Bahrami
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  3. Hadis Karimipour
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Correspondence to Hossein Mohammadi Rouzbahani .

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Editors and Affiliations

  1. Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada

    Hadis Karimipour

  2. North York, ON, Canada

    Farnaz Derakhshan

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Rouzbahani, H.M., Bahrami, A.H., Karimipour, H. (2021). A Snapshot Ensemble Deep Neural Network Model for Attack Detection in Industrial Internet of Things. In: Karimipour, H., Derakhshan, F. (eds) AI-Enabled Threat Detection and Security Analysis for Industrial IoT . Springer, Cham. https://doi.org/10.1007/978-3-030-76613-9_10

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  • DOI: https://doi.org/10.1007/978-3-030-76613-9_10

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