Research on Intrusion Detection Technology of Computing Nodes in Digital Power Grid based on Artificial Intelligence
DOI:
https://doi.org/10.4108/eetsis.v10i3.3092Keywords:
Intrusion detection, artificial intelligence, performance analysisAbstract
This paper aims to investigate an intrusion detection network for digital power grid networks, which consists of an edge server and two computational nodes that work collaboratively to detect any potential intrusion in the network. The primary objective of this study is to enhance the effectiveness of intrusion detection in the network. To achieve this objective, we first define the outage probability of the intrusion detection system under consideration. This is done to provide a measure of the probability that the system fails to detect an intrusion when it occurs. We then derive a closed-form expression for the outage probability to enable further analysis on the system behavior. Since the system resources, such as transmit power, are limited, we further design a transmit power allocation strategy to improve the system performance. This strategy seeks to optimize the allocation of transmit power across the different nodes of the intrusion detection network to maximize the likelihood of detecting intrusions while minimizing the resource usage. Finally, to evaluate the performance of the proposed system, we conduct simulations and provide results that demonstrate the accuracy of the closed-form expression and the effectiveness of the transmit power allocation strategy. These simulation results serve as evidence of the efficacy of the proposed approach in detecting intrusions in a resource-constrained network, especially for the digital power grid networks.
References
H. Yao, X. Li, and X. Yang, “Physics-aware learning-based vehicle trajectory prediction of congested traffic in a connected vehicle environment,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 102–112, 2023.
J. Lin, G. Wang, S. Atapattu, R. He, G. Yang, and C. Tellambura, “Transmissive metasurfaces assisted wireless communications on railways: Channel strength evaluation and performance analysis,” IEEE Trans. Commun., 2023.
L. F. Abanto-Leon, A. Asadi, A. Garcia-Saavedra, G. H. Sim, and M. Hollick, “Radiorchestra: Proactive management of millimeter-wave self-backhauled small cells via joint optimization of beamforming, user association, rate selection, and admission control,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 153–173, 2023.
Y. Sun, D. Wu, X. S. Fang, and J. Ren, “On-glass grid structure and its application in highly-transparent antenna for internet of vehicles,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 93–101, 2023.
Z. Na, B. Li, X. Liu, J. Wan, M. Zhang, Y. Liu, and B. Mao, “Uav-based wide-area internet of things: An integrated deployment architecture,” IEEE Netw., vol. 35, no. 5, pp. 122–128, 2021.
W. Wu, F. Yang, F. Zhou, Q. Wu, and R. Q. Hu, “Intelligent resource allocation for IRS-enhanced OFDM communication systems: A hybrid deep reinforcement learning approach,” IEEE Trans. Wirel. Commun., vol. PP, no. 99, pp. 1–10, 2023.
M. Sun, W. Liu, J. Liu, and C. Hao, “Complex parameter rao, wald, gradient, and durbin tests for multichannel signal detection,” IEEE Trans. Signal Process., vol. 70, pp. 117–131, 2022.
W. Zhou, C. Li, and M. Hua, “Worst-case robust MIMO transmission based on subgradient projection,” IEEE Commun. Lett., vol. 25, no. 1, pp. 239–243, 2021.
Z. Xuan and K. Narayanan, “Low-delay analog joint source-channel coding with deep learning,” IEEE Trans. Commun., vol. 71, no. 1, pp. 40–51, 2023.
J. Shao, Y. Mao, and J. Zhang, “Task-oriented communi-cation for multidevice cooperative edge inference,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 73–87, 2023.
F. Liu, Y. Liu, A. Li, C. Masouros, and Y. C. Eldar, “Cramér-rao bound optimization for joint radar-communication beamforming,” IEEE Trans. Signal Pro-cess., vol. 70, pp. 240–253, 2022.
W. Hong, J. Yin, M. You, H. Wang, J. Cao, J. Li, and M. Liu, “Graph intelligence enhanced bi-channel insider threat detection,” in Network and System Security: 16th International Conference, NSS 2022, Denarau Island, Fiji, December 9–12, 2022, Proceedings. Springer, 2022, pp. 86–102.
J. Yin, M. Tang, J. Cao, M. You, H. Wang, and M. Alazab, “Knowledge-driven cybersecurity intelligence: software vulnerability co-exploitation behaviour discovery,” IEEE Transactions on Industrial Informatics, 2022.
J. Yin, M. Tang, J. Cao, H. Wang, M. You, and Y. Lin, “Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning,” World Wide Web, pp. 1–23, 2022.
Z. Song, J. An, G. Pan, S. Wang, H. Zhang, Y. Chen, and M. Alouini, “Cooperative satellite-aerial-terrestrial systems: A stochastic geometry model,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 220–236, 2023.
D. Orlando, S. Bartoletti, I. Palamà, G. Bianchi, and N. Blefari-Melazzi, “Innovative attack detection solutions for wireless networks with application to location security,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 205–219, 2023.
M. E. Gonzalez, J. F. Silva, M. Videla, and M. E. Orchard, “Data-driven representations for testing independence: Modeling, analysis and connection with mutual informa-tion estimation,” IEEE Trans. Signal Process., vol. 70, pp. 158–173, 2022.
J. Ren, X. Lei, Z. Peng, X. Tang, and O. A. Dobre, “Ris-assisted cooperative NOMA with SWIPT,” IEEE Wireless Communications Letters, 2023.
W. Xu, Z. Yang, D. W. K. Ng, M. Levorato, Y. C. Eldar, and M. Debbah, “Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,” IEEE J. Sel. Top. Signal Process., vol. 17, no. 1, pp. 9–39, 2023.
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Copyright (c) 2023 Xubin Lin, Situo Zhang, Feifei Hu, Liu Wu
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