Analysis and Application of Chaotic Genetic Algorithm Based on Network Security in The Research of Resilience of Cluster Networks
DOI:
https://doi.org/10.13052/jcsm2245-1439.1344Keywords:
Unmanned collaboration, network modeling, system capability, network survivabilityAbstract
With the wide application of UAV, in the actual flight process, UAV needs to calculate the safe path according to its own position, environment, obstacles and other information. Due to the complex and changeable scene and environment of UAV mission execution, it is very important to select an appropriate UAV path planning algorithm. This paper aims at the path planning problem of multiple UAVs in a complex three-dimensional environment to ensure that multiple UAVs reach the mission location from different angles. Taking the chaotic genetic algorithm in network security protection as the main body, the operation difficulty of the algorithm is reduced, and the solution speed and accuracy of the algorithm are improved. The path length obtained by the proposed algorithm is 8.4% less than that of the ABC algorithm, 11.3% less than that of the PSO algorithm, and 4.2% less than that of the BABC algorithm. The system running time of the improved algorithm is also reduced by 27% to 45% compared with other algorithms. In terms of unmanned cooperation, this paper proposes a system capability based on network modeling to improve the cooperative combat capability of multiple UAVs. By establishing a network model, information sharing, collaborative decision-making and collaborative decision-making between drones are realized, thereby improving the effectiveness of the entire system. At the same time, this paper also considers the problem of network survivability. By introducing redundant design and fault recovery mechanism, the robustness and reliability of the system are enhanced.
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Abdulhae, O. T., Mandeep, J. S., and Islam, M. (2022). Cluster-based routing protocols for flying ad hoc networks (FANETs). IEEE Access, 10, 32981–33004.
Li, J., Kang, H., Sun, G., Liang, S., Liu, Y., and Zhang, Y. (2021, May). Physical layer secure communications based on collaborative beamforming for UAV networks: A multi-objective optimization approach. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications (pp. 1–10). IEEE.
Pham, Q. V., Nguyen, D. C., Mirjalili, S., Hoang, D. T., Nguyen, D. N., Pathirana, P. N., and Hwang, W. J. (2021). Swarm intelligence for next-generation networks: Recent advances and applications. Journal of Network and Computer Applications, 191, 103141.
Phadke, A., and Medrano, F. A. (2022). Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms. Drones, 6(11), 340.
Alsolai, H., Alzahrani, J. S., Maray, M., Alghamdi, M., Qahmash, A., Alnfiai, M. M., … and Mustafa Hilal, A. (2022). Enhanced artificial gorilla troops optimizer based clustering protocol for UAV-assisted intelligent vehicular network. Drones, 6(11), 358.
Alrayes, F. S., Alotaibi, S. S., Alissa, K. A., Maashi, M., Alhogail, A., Alotaibi, N., … and Motwakel, A. (2022). Artificial intelligence-based secure communication and classification for drone-enabled emergency monitoring systems. Drones, 6(9), 222.
Singh, S. P., Piras, G., Viriyasitavat, W., Kariri, E., Yadav, K., Dhiman, G., … and Khan, S. B. (2023). Cyber Security and 5G-assisted Industrial Internet of Things using Novel Artificial Adaption based Evolutionary Algorithm. Mobile Networks and Applications, 1–17.
Abualigah, L., Diabat, A., Sumari, P., and Gandomi, A. H. (2021). Applications, deployments, and integration of internet of drones (iod): a review. IEEE Sensors Journal, 21(22), 25532–25546.
Pan, Y., Yang, Y., and Li, W. (2021). A deep learning trained by genetic algorithm to improve the efficiency of path planning for data collection with multi-UAV. Ieee Access, 9, 7994–8005.
Stolfi, D. H., Brust, M. R., Danoy, G., and Bouvry, P. (2020, May). Competitive evolution of a UAV swarm for improving intruder detection rates. In 2020 IEEE international parallel and distributed processing symposium workshops (IPDPSW) (pp. 528–535). IEEE.
Shafique, A., Mehmood, A., and Elhadef, M. (2021). Survey of security protocols and vulnerabilities in unmanned aerial vehicles. IEEE Access, 9, 46927–46948.
Hadi, H. J., Cao, Y., Nisa, K. U., Jamil, A. M., and Ni, Q. (2023). A comprehensive survey on security, privacy issues and emerging defence technologies for UAVs. Journal of Network and Computer Applications, 213, 103607.
Shehab, M., Abualigah, L., Al Hamad, H., Alabool, H., Alshinwan, M., and Khasawneh, A. M. (2020). Moth–flame optimization algorithm: variants and applications. Neural Computing and Applications, 32, 9859–9884.
Khan, A., Zhang, J., Ahmad, S., Memon, S., Qureshi, H. A., and Ishfaq, M. (2022). Dynamic positioning and energy-efficient path planning for disaster scenarios in 5G-assisted multi-UAV environments. Electronics, 11(14), 2197.
Stolfi, D. H., Brust, M. R., Danoy, G., and Bouvry, P. (2021, September). Improving Pheromone Communication for UAV Swarm Mobility Management. In International Conference on Computational Collective Intelligence (pp. 228–240). Cham: Springer International Publishing.
Sun, G., Li, J., Liu, Y., Liang, S., and Kang, H. (2021). Time and energy minimization communications based on collaborative beamforming for UAV networks: A multi-objective optimization method. IEEE Journal on Selected Areas in Communications, 39(11), 3555–3572.
Zhang, H., and Li, P. (2023). Research on Location Privacy Protection Technology in Wireless Sensor Networks Based on Big Data. Journal of Cyber Security and Mobility, 12(06), 845–868.
Quan, R., Liang, W., Wang, J., Li, X., and Chang, Y. (2024). An enhanced fault diagnosis method for fuel cell system using a kernel extreme learning machine optimized with improved sparrow search algorithm. International Journal of Hydrogen Energy, 50, 1184–1196.
Chamola, V., Hassija, V., Gupta, S., Goyal, A., Guizani, M., and Sikdar, B. (2020). Disaster and pandemic management using machine learning: a survey. IEEE Internet of Things Journal, 8(21), 16047–16071.
Chanal, P. M., Kakkasageri, M. S., and Manvi, S. K. S. (2021). Security and privacy in the internet of things: computational intelligent techniques-based approaches. In Recent Trends in Computational Intelligence Enabled Research (pp. 111–127). Academic Press.
Banafshehvaragh, S. T., and Rahmani, A. M. (2023). Intrusion, anomaly, and attack detection in smart vehicles. Microprocessors and Microsystems, 96, 104726.
Li, H., Sun, Q., Zhong, Y., Huang, Z., and Zhang, Y. (2023). A soft resource optimization method for improving the resilience of UAV swarms under continuous attack. Reliability Engineering & System Safety, 237, 109368.
Shi, Y., Lin, Y., Li, B., and Li, R. Y. M. (2022). A bi-objective optimization model for the medical supplies’ simultaneous pickup and delivery with drones. Computers & Industrial Engineering, 171, 108389.
Kuru, K., Ansell, D., Khan, W., and Yetgin, H. (2019). Analysis and optimization of unmanned aerial vehicle swarms in logistics: An intelligent delivery platform. Ieee Access, 7, 15804–15831.
Li, R., Wang, A., Sun, G., Li, J., and Zheng, X. (2022, June). Bi-objective Optimization for Collaborative UAV Secure Communication under Forest Channel. In 2022 IEEE Symposium on Computers and Communications (ISCC) (pp. 01–07). IEEE.
Zhou, G., Zhang, W., Chen, A., He, M., and Ma, X. (2019). Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion. IEEE access, 7, 143190–143206.
Lu, Y. (2023). Security and Privacy of Internet of Things: A Review of Challenges and Solutions. Journal of Cyber Security and Mobility, 12(06), 813–844.
Mukherjee, A., Misra, S., and Raghuwanshi, N. S. (2019). A survey of unmanned aerial sensing solutions in precision agriculture. Journal of Network and Computer Applications, 148, 102461.
Yazdani, M., Babagolzadeh, M., Kazemitash, N., and Saberi, M. (2019). Reliability estimation using an integrated support vector regression–variable neighborhood search model. Journal of Industrial Information Integration, 15, 103–110.
Orlovskyi, R., Kharytonov, S., Samoshchenko, I., Us, O., and Iemelianenko, V. (2023). Countering Cybercrime Under Martial Law. Journal of Cyber Security and Mobility, 12(06), 893–910.
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