Abstract
Aiming at the problem of poor intrusion prevention in power UAV network communication, this paper proposes an optimization algorithm for power UAV network communication intrusion prevention based on artificial intelligence. Detect the abnormal data of power UAV network communication, strengthen the data filtering and management, and carry out adaptive filtering for abnormal areas to achieve effective data preprocessing. Furthermore, it captures communication intrusion data, transmits data filter signal, adjusts detection algorithm, monitors internal signal and avoids bad signal. The optimal intrusion threshold is calculated, and the collected data is adjusted on the basis of artificial intelligence algorithm to obtain the final parameters of the algorithm. Experimental results show that the convergence value of the algorithm is 3 × 105, which has low convergence. Strong anti invasion ability and good practical application prospects. The results show that the algorithm based on artificial intelligence has good performance.
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References
Chen, Z., et al.: UAV network intrusion detection method based on spatio-temporal graph convolutional network. J. Beijing Univ. Aeronaut. Astronaut. 47(5), 1068–1076 (2021)
Cao, S., et al.: Secure communication in cognitive radio network assisted by cooperative relay and UAV. Comput. Eng. 47(6), 203–209 (2021)
Liu, S., He, T., Dai, J.: A survey of CRF algorithm based knowledge extraction of elementary mathematics in Chinese. Mob. Netw. Appl. 26(5), 1891–1903 (2021). https://doi.org/10.1007/s11036-020-01725-x
Husnain, G., Anwar, S.: An intelligent probabilistic whale optimization algorithm (i-WOA) for clustering in vehicular ad hoc networks. Int. J. Wireless Inf. Netw. 29(2), 143–156 (2022)
Liu, B., Zhu, Q., Zhu, H.: Trajectory optimization and resource allocation for UAV-assisted relaying communications. Wireless Netw. 26(1), 739–749 (2020)
Gao, P., Li, J., Liu, S.: An introduction to key technology in artificial intelligence and big data driven e-learning and e-education. Mobile Netw. Appl. 26(5), 2123–2126 (2021). https://doi.org/10.1007/s11036-021-01777-7
Zahra, M., Ibrahim, Z., Al-Safi, A.: Using a hybrid algorithm and feature selection for network anomaly intrusion detection. J. Mech. Eng. Res. Dev. 44(4), 253–262 (2021)
Wang, Y., et al.: Aerodynamic optimization of a SCO2radial-inflow turbine based on an improved simulated annealing algorithm. Proc. Inst. Mech. Eng., Part A: J. Power Energy 235(5), 1039–1052 (2021)
Liu, S., et al.: Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans. Multimedia 23, 2188–2198 (2021)
Aledhari, M., Razzak, R., Parizi, R.M.: Machine learning for network application security: empirical evaluation and optimization. Comput. Electr. Eng. 91(16), 107052 (2021)
Wang, J.F., Jia, G.W., Lin, J.C., Hou, Z.: Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm. J. Cent. South Univ. 27(2), 432–448 (2020)
Cong, J., Wang, X., Wan, L., Huang, M.: Neural network-aided sparse convex optimization algorithm for fast DOA estimation. Trans. Inst. Meas. Control 44(8), 1649–1655 (2022). https://doi.org/10.1177/01423312211049067
Li, S.S., Wang, X.K., Fu, Z.Q.: Construction of an extended target echo platform based on FEKO and MATLAB. Comput. Simul. 39(1), 5.10–13+33 (2022)
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Hu, G., Lin, Z., Guo, Z., Xu, R., Zhang, X. (2023). Research on Intrusion Prevention Optimization Algorithm of Power UAV Network Communication Based on Artificial Intelligence. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_21
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DOI: https://doi.org/10.1007/978-3-031-20102-8_21
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