Volume 8 • Issue 1 • PP: 66-74 • 2023
Securing Wireless Sensor Networks Against DoS attacks in Industrial 4.0
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Wireless Sensor Networks (WSNs) play a vital role in Industrial 4.0 by facilitating significant data collection for monitoring and control purposes. However, their distributed and resource-constrained nature makes WSNs vulnerable to Denial-of-Service (DoS) attacks, which can impede their normal operation and jeopardize their functionality. To address this issue, we propose a new machine learning (ML) approach that enhances the security of WSNs against DoS attacks in Industrial 4.0. Our approach incorporates a spatial learning unit, which captures the positional information in WSN traffic flows, and a temporal learning unit which captures time interdependency features within periods of traffic flows. To evaluate the proposed approach, we tested it on a publicly available dataset. The results demonstrate that it achieves a high detection rate while maintaining a low false alarm rate. Moreover, our Intrusion Detection System (IDS) exhibits good scalability and robustness against various DoS attacks. Our approach provides a reliable and effective solution to secure WSNs in Industrial 4.0 against DoS attacks and can be further developed and tested in various real-world scenarios.
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References
[1]. Z. Qin, D. Wu, Z. Xiao, B. Fu, and Z. Qin, "Modeling and Analysis of Data Aggregation From Convergecast in Mobile Sensor Networks for Industrial IoT," in IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4457-4467, Oct. 2018, doi: 10.1109/TII.2018.2846687.
[2]. Almuntasheri, S., & Alenazi, M. J. (2022). Software-Defined Network-Based Energy-Aware Routing Method for Wireless Sensor Networks in Industry 4.0. Applied Sciences, 12(19), 10073.
[3]. Lin, C. C., Deng, D. J., Chen, Z. Y., & Chen, K. C. (2016). Key design of driving industry 4.0: Joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks. IEEE Communications Magazine, 54(10), 46-52.
[4]. Majid, M., Habib, S., Javed, A. R., Rizwan, M., Srivastava, G., Gadekallu, T. R., & Lin, J. C. W. (2022). Applications of wireless sensor networks and internet of things frameworks in the industry revolution 4.0: A systematic literature review. Sensors, 22(6), 2087.
[5]. De Beelde, B., Plets, D., & Joseph, W. (2021). Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0. Applied Sciences, 11(23), 11248.
[6]. G. Künzel, L. S. Indrusiak and C. E. Pereira, "Latency and Lifetime Enhancements in Industrial Wireless Sensor Networks: A Q-Learning Approach for Graph Routing," in IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 5617-5625, Aug. 2020, doi: 10.1109/TII.2019.2941771.
[7]. H. Wang, F. Yu, M. Li and Y. Zhong, "Clock Skew Estimation for Timestamp-Free Synchronization in Industrial Wireless Sensor Networks," in IEEE Transactions on Industrial Informatics, vol. 17, no. 1, pp. 90-99, Jan. 2021, doi: 10.1109/TII.2020.2975289.
[8]. Y. Zou and G. Wang, "Intercept Behavior Analysis of Industrial Wireless Sensor Networks in the Presence of Eavesdropping Attack," in IEEE Transactions on Industrial Informatics, vol. 12, no. 2, pp. 780-787, April 2016, doi: 10.1109/TII.2015.2399691.
[9]. S. Zoppi, A. Van Bemten, H. M. Gürsu, M. Vilgelm, J. Guck and W. Kellerer, "Achieving Hybrid Wired/Wireless Industrial Networks With WDetServ: Reliability-Based Scheduling for Delay Guarantees," in IEEE Transactions on Industrial Informatics, vol. 14, no. 5, pp. 2307-2319, May 2018, doi: 10.1109/TII.2018.2803122.
[10]. Fotohi, R., Firoozi Bari, S., & Yusefi, M. (2020). Securing wireless sensor networks against denial‐of‐sleep attacks using RSA cryptography algorithm and interlock protocol. International Journal of Communication Systems, 33(4), e4234.
[11]. H. Wang, L. Shao, M. Li, B. Wang and P. Wang, "Estimation of Clock Skew for Time Synchronization Based on Two-Way Message Exchange Mechanism in Industrial Wireless Sensor Networks," in IEEE Transactions on Industrial Informatics, vol. 14, no. 11, pp. 4755-4765, Nov. 2018, doi: 10.1109/TII.2018.2799595.
[12]. T. M. Chiwewe, C. F. Mbuya and G. P. Hancke, "Using Cognitive Radio for Interference-Resistant Industrial Wireless Sensor Networks: An Overview," in IEEE Transactions on Industrial Informatics, vol. 11, no. 6, pp. 1466-1481, Dec. 2015, doi: 10.1109/TII.2015.2491267.
[13]. M. Magno, D. Boyle, D. Brunelli, E. Popovici and L. Benini, "Ensuring Survivability of Resource-Intensive Sensor Networks Through Ultra-Low Power Overlays," in IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 946-956, May 2014, doi: 10.1109/TII.2013.2295198.
[14]. Khalaf, O. I., & Abdulsahib, G. M. (2021). Optimized dynamic storage of data (ODSD) in IoT based on blockchain for wireless sensor networks. Peer-to-Peer Networking and Applications, 14, 2858-2873.
[15]. Elsayed abou Elwafa, S. A., Aboul Fotouh Saleh, S., Mohamed Abd El-razk, E. E., & Elatawy, S. M. (2022). Securing Management Information Systems Using Blockchain Technology. International Journal of Artificial Intelligence and Education Technology, 1(2), 22-35. https://doi.org/10.54216/IJAIET.010202
[16]. Zaher, M., & El-Khameesy ElGhitany, N. (2021). Blockchain Communication Platform Selection in IoT Healthcare Industry using MARCOS. International Journal of Wireless and Ad Hoc Communication, 2(1), 49-57. https://doi.org/10.54216/IJWAC.020104
[17]. Fattah, S., Gani, A., Ahmedy, I., Idris, M. Y. I., & Targio Hashem, I. A. (2020). A survey on underwater wireless sensor networks: requirements, taxonomy, recent advances, and open research challenges. Sensors, 20(18), 5393.
[18]. Ma, K., Li, Z., Liu, P., Yang, J., Geng, Y., Yang, B., & Guan, X. (2021). Reliability-constrained throughput optimization of industrial wireless sensor networks with energy harvesting relay. IEEE Internet of Things Journal, 8(17), 13343-13354.
[19]. A. Sariga, & J. Uthayakumar. (2020). Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks. International Journal of Wireless and Ad Hoc Communication, 1(1), 33-46.
[20]. Almomani, I., Al-Kasasbeh, B. and Al-Akhras, M., 2016. WSN-DS: A dataset for intrusion detection systems in wireless sensor networks. Journal of Sensors, 2016.
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