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Blockchain and Deep Learning-Based IDS for Securing SDN-Enabled Industrial IoT Environments (2401.00468v1)

Published 31 Dec 2023 in cs.CR and cs.NI

Abstract: The industrial Internet of Things (IIoT) involves the integration of Internet of Things (IoT) technologies into industrial settings. However, given the high sensitivity of the industry to the security of industrial control system networks and IIoT, the use of software-defined networking (SDN) technology can provide improved security and automation of communication processes. Despite this, the architecture of SDN can give rise to various security threats. Therefore, it is of paramount importance to consider the impact of these threats on SDN-based IIoT environments. Unlike previous research, which focused on security in IIoT and SDN architectures separately, we propose an integrated method including two components that work together seamlessly for better detecting and preventing security threats associated with SDN-based IIoT architectures. The two components consist in a convolutional neural network-based Intrusion Detection System (IDS) implemented as an SDN application and a Blockchain-based system (BS) to empower application layer and network layer security, respectively. A significant advantage of the proposed method lies in jointly minimizing the impact of attacks such as command injection and rule injection on SDN-based IIoT architecture layers. The proposed IDS exhibits superior classification accuracy in both binary and multiclass categories.

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References (22)
  1. Y. Chen, T. Sun, B. Yang, and T. Taleb, “Joint caching and computing service placements for edge-enabled iot based on deep reinforcement learning,” in IEEE IoT Journal, vol. 9, no. 19, pp. 19 501 – 19 514, Oct. 2022.
  2. Q. Guo, R. Gu, H. Yu, T. Taleb, and Y. Ji, “Probabilistic-assured resource provisioning with customizable hybrid isolation for vertical industrial slicing,” in IEEE TNSM, vol. 20, no. 2, pp. 1660 – 1675, Jun. 2023.
  3. J. Sengupta, S. Ruj, and S. Das Bit, “A comprehensive survey on attacks, security issues and blockchain solutions for iot and iiot,” in Journal of Network and Computer Applications, vol. 149, p. 102481, Jan. 2020.
  4. M. A. Khan, M. R. Karim, and Y. Kim, “A scalable and hybrid intrusion detection system based on the convolutional-lstm network,” in Symmetry, vol. 11, no. 4, p. 583, Apr. 2019.
  5. O. Abdul Wahab, A. Mourad, H. Otrok, and T. Taleb, “Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems,” in IEEE COMST, vol. 23, no. 2, pp. 1342 – 1397, Secondquarter 2021.
  6. Y. Lai, J. Zhang, and Z. Liu, “Industrial anomaly detection and attack classification method based on convolutional neural network,” in Security and Communication Networks, vol. 2019, pp. 1–11, Sept. 2019.
  7. C. Benzaid, T. Taleb, and J. Song, “AI-based Autonomic & Scalable Security Management Architecture for Secure Network Slicing in B5G,” IEEE Network Magazine, vol. 36, no. 6, pp. 165 – 174, Nov./Dec. 2022.
  8. M. J. Islam, A. Rahman, S. Kabir, M. R. Karim, U. K. Acharjee, M. K. Nasir, S. S. Band, M. Sookhak, and S. Wu, “Blockchain-sdn-based energy-aware and distributed secure architecture for iot in smart cities,” in IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3850–3864, Mar. 2021.
  9. O. Hireche, C. Benzaïd, and T. Taleb, “Deep data plane programming and ai for zero-trust self-driven networking in beyond 5g,” in Computer Networks, vol. 203, p. 108668, Feb. 2022.
  10. Y. Liu, B. Zhao, P. Zhao, P. Fan, and H. Liu, “A survey: Typical security issues of software-defined networking,” in China Communications, vol. 16, no. 7, pp. 13–31, Jul. 2019.
  11. M. L. Adjou, C. Benzaïd, and T. Taleb, “Topotrust: A blockchain-based trustless and secure topology discovery in sdns,” in in Proc. 2022 International Wireless Communications and Mobile Computing (IWCMC), Dubrovnik, Croatia, Jul. 2022.
  12. A. Derhab, M. Guerroumi, M. Belaoued, and O. Cheikhrouhou, “Bmc-sdn: blockchain-based multicontroller architecture for secure software-defined networks,” in Wireless Communications and Mobile Computing, vol. 2021, pp. 1–12, Apr. 2021.
  13. A. Bose, G. S. Aujla, M. Singh, N. Kumar, and H. Cao, “Blockchain as a service for software defined networks: A denial of service attack perspective,” in in Proc. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress, Fukuoka, Japan, Aug. 2019.
  14. A. Derhab, M. Guerroumi, A. Gumaei, L. Maglaras, M. A. Ferrag, M. Mukherjee, and F. A. Khan, “Blockchain and random subspace learning-based ids for sdn-enabled industrial iot security,” in Sensors, vol. 19, no. 14, p. 3119, Jul. 2019.
  15. T. Morris, “Industrial control system (ics) cyber attack datasets,” in 1st International Symposium for ICS & SCADA Cyber Security Research 2013 (ICS-CSR 2013) 1, 2022. [Online]. Available: https://sites.google.com/a/uah.edu/tommy-morris-uah/ics-data-sets
  16. S. V. B. Rakas, M. D. Stojanović, and J. D. Marković-Petrović, “A review of research work on network-based scada intrusion detection systems,” in IEEE Access, vol. 8, pp. 93 083–93 108, May 2020.
  17. B. Alotaibi and M. Alotaibi, “A stacked deep learning approach for iot cyberattack detection,” in Journal of Sensors, vol. 2020, pp. 1–10, Sept. 2020.
  18. A. Al Hayajneh, M. Z. A. Bhuiyan, and I. McAndrew, “Improving internet of things (iot) security with software-defined networking (sdn),” in Computers, vol. 9, no. 1, p. 8, Feb. 2020.
  19. A. K. Nair and J. N. D. J. Jingle, “Distributed denial-of-service detection and mitigation using software-defined network and internet of things,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 1, p. 10, Sept. 2019.
  20. K. Haseeb, I. Ahmad, I. I. Awan, J. Lloret, and I. Bosch, “A machine learning sdn-enabled big data model for iomt systems,” in Electronics, vol. 10, no. 18, p. 2228, Sept. 2021.
  21. S. Scott-Hayward, S. Natarajan, and S. Sezer, “A survey of security in software defined networks,” in IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 623–654, Firstquarter 2016.
  22. T. H. Morris, Z. Thornton, and I. Turnipseed, “Industrial control system simulation and data logging for intrusion detection system research,” in Proc. 7th annual southeastern cyber security summit, Alabama, USA, Jun. 2015.
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Authors (3)
  1. Samira Kamali Poorazad (2 papers)
  2. Chafika Benzaıd (54 papers)
  3. Tarik Taleb (30 papers)
Citations (2)
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