Secure communication routing and attack detection in UAV networks using Gannet Walruses optimization algorithm and Sheppard Convolutional Spinal Network

Unmanned Aerial vehicles (UAV) are high-speed moving machines that attained rapid growth in various activities and are considered an integral component in the Satellite-Air -Ground-Sea (SAGS) incorporated network. However, UAVs are affected by communication delays and malicious attacks. Therefore, a...

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Vydané v:	Peer-to-peer networking and applications Ročník 17; číslo 5; s. 3269 - 3285
Hlavní autori:	Renu, Yuvaraj, Sarveshwaran, Velliangiri
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.09.2024 Springer Nature B.V
Predmet:	Algorithms Artificial neural networks Communication Communication networks Communications Engineering Computer Communication Networks Data communication Decision making Engineering Information Systems and Communication Service Multiple objective analysis Networks Neural networks Nodes Optimization Optimization algorithms Packets (communication) Recurrent neural networks Routing (telecommunications) Signal,Image and Speech Processing Special Issue on 2 - Track on Security and Privacy Unmanned aerial vehicles Agent-based security Unmanned aerial vehicles attacks Sheppard Convolutional Spinal Network Gannet Walruses Optimization Secure routing Deep Recurrent Neural Network
ISSN:	1936-6442, 1936-6450
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Abstract	Unmanned Aerial vehicles (UAV) are high-speed moving machines that attained rapid growth in various activities and are considered an integral component in the Satellite-Air -Ground-Sea (SAGS) incorporated network. However, UAVs are affected by communication delays and malicious attacks. Therefore, an adequate and secure communication routing and attack detection model is necessary for UAV communication networks. This research described a novel approach for initiating secure communication in UAV networks namely Gannet Walruses Optimization Algorithm + Sheppard Convolutional Spinal Network (GWOA + ShCSpinalNet). Initially, the UAV network is simulated, and the data packets are transmitted among the nodes using optimal routing paths. An optimal routing path is computed using the Gannet Walruses Optimization Algorithm (GWOA) by considering some multi-objective functions through the Deep Recurrent Neural Network (DRNN). The developed GWAO integrates Gannet Optimization (GOA) and Walruses Optimization (WaOA). The data communication is done through monitoring agents. The newly devised Sheppard Convolutional Spinal Network ( ShCSpinalNet) is utilized as a decision-making agent for malicious attack detection. The attributes considered for decision-making are round trip time, packet delivery ratio, the strength of the signal, the size of the packet, and the number of incoming packets. Once the SpinalNet categorizes the normal and attacked nodes the defense agent is implemented for attack migration. The ShCSpinalNet is devised by the combination of the Sheppard Convolutional Neural Network and Spinal Network. The GWOA + ShCSpinalNet accomplishes a diminished delay of 0.614 s, an increased detection rate of 0.930%, an energy of 0.439 J, and a Packet Delivery Ratio (PDR) of 0.749.
AbstractList	Unmanned Aerial vehicles (UAV) are high-speed moving machines that attained rapid growth in various activities and are considered an integral component in the Satellite-Air -Ground-Sea (SAGS) incorporated network. However, UAVs are affected by communication delays and malicious attacks. Therefore, an adequate and secure communication routing and attack detection model is necessary for UAV communication networks. This research described a novel approach for initiating secure communication in UAV networks namely Gannet Walruses Optimization Algorithm + Sheppard Convolutional Spinal Network (GWOA + ShCSpinalNet). Initially, the UAV network is simulated, and the data packets are transmitted among the nodes using optimal routing paths. An optimal routing path is computed using the Gannet Walruses Optimization Algorithm (GWOA) by considering some multi-objective functions through the Deep Recurrent Neural Network (DRNN). The developed GWAO integrates Gannet Optimization (GOA) and Walruses Optimization (WaOA). The data communication is done through monitoring agents. The newly devised Sheppard Convolutional Spinal Network ( ShCSpinalNet) is utilized as a decision-making agent for malicious attack detection. The attributes considered for decision-making are round trip time, packet delivery ratio, the strength of the signal, the size of the packet, and the number of incoming packets. Once the SpinalNet categorizes the normal and attacked nodes the defense agent is implemented for attack migration. The ShCSpinalNet is devised by the combination of the Sheppard Convolutional Neural Network and Spinal Network. The GWOA + ShCSpinalNet accomplishes a diminished delay of 0.614 s, an increased detection rate of 0.930%, an energy of 0.439 J, and a Packet Delivery Ratio (PDR) of 0.749. Unmanned Aerial vehicles (UAV) are high-speed moving machines that attained rapid growth in various activities and are considered an integral component in the Satellite-Air -Ground-Sea (SAGS) incorporated network. However, UAVs are affected by communication delays and malicious attacks. Therefore, an adequate and secure communication routing and attack detection model is necessary for UAV communication networks. This research described a novel approach for initiating secure communication in UAV networks namely Gannet Walruses Optimization Algorithm + Sheppard Convolutional Spinal Network (GWOA + ShCSpinalNet). Initially, the UAV network is simulated, and the data packets are transmitted among the nodes using optimal routing paths. An optimal routing path is computed using the Gannet Walruses Optimization Algorithm (GWOA) by considering some multi-objective functions through the Deep Recurrent Neural Network (DRNN). The developed GWAO integrates Gannet Optimization (GOA) and Walruses Optimization (WaOA). The data communication is done through monitoring agents. The newly devised Sheppard Convolutional Spinal Network (ShCSpinalNet) is utilized as a decision-making agent for malicious attack detection. The attributes considered for decision-making are round trip time, packet delivery ratio, the strength of the signal, the size of the packet, and the number of incoming packets. Once the SpinalNet categorizes the normal and attacked nodes the defense agent is implemented for attack migration. The ShCSpinalNet is devised by the combination of the Sheppard Convolutional Neural Network and Spinal Network. The GWOA + ShCSpinalNet accomplishes a diminished delay of 0.614 s, an increased detection rate of 0.930%, an energy of 0.439 J, and a Packet Delivery Ratio (PDR) of 0.749.
Author	Renu, Yuvaraj Sarveshwaran, Velliangiri
Author_xml	– sequence: 1 givenname: Yuvaraj surname: Renu fullname: Renu, Yuvaraj organization: Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology – sequence: 2 givenname: Velliangiri surname: Sarveshwaran fullname: Sarveshwaran, Velliangiri email: velliangiris@gmail.com organization: Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Department of Computer Science and Information Engineering, National Chung Cheng University
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Cites_doi	10.1007/s11277-021-08839-9 10.22967/HCIS.2022.12.034 10.1155/2022/5428280 10.1007/s11227-020-03462-0 10.1155/2022/4782850 10.3390/electronics10141715 10.1109/TNSM.2024.3357824 10.1038/s41598-023-35863-5 10.3390/app13042682 10.3390/s18051413 10.3390/drones3030059 10.1016/j.adhoc.2021.102560 10.1155/2021/4734023 10.1007/s11831-020-09418-0 10.1016/j.matcom.2022.06.007 10.1080/01969722.2022.2151189 10.1016/j.vehcom.2020.100267 10.1109/MWC.001.1900045 10.1002/int.22800 10.1016/j.adhoc.2022.102790
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Cybernet Syst 1–28 – reference: PanJSZhangLGWangRBSnášelVChuSCGannet optimization algorithm: a new metaheuristic algorithm for solving engineering optimization problemsMath Comput Simul2022202343373444516910.1016/j.matcom.2022.06.007 – reference: PangXLiuMLiZGaoBGuoXGeographic position based hopeless opportunistic routing for UAV networksAd Hoc Netw202112010.1016/j.adhoc.2021.102560 – reference: RyuJImproved Image Quality Assessment by Utilizing Pre-Trained Architecture Features with Unified Learning MechanismAppl Sci2023134268210.3390/app13042682 – reference: Kabir HD, Abdar M, Khosravi A, Jalali SMJ, Atiya AF, Nahavandi S, Srinivasan D (2022) Spinalnet: Deep neural network with gradual input. IEEE Transac Artif Intelligence (99):1–13 – reference: Zhuo R, Song S, Xu Y (2022) UAV communication network modeling and energy consumption optimization based on routing algorithm. 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Snippet	Unmanned Aerial vehicles (UAV) are high-speed moving machines that attained rapid growth in various activities and are considered an integral component in the...
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SubjectTerms	Algorithms Artificial neural networks Communication Communication networks Communications Engineering Computer Communication Networks Data communication Decision making Engineering Information Systems and Communication Service Multiple objective analysis Networks Neural networks Nodes Optimization Optimization algorithms Packets (communication) Recurrent neural networks Routing (telecommunications) Signal,Image and Speech Processing Special Issue on 2 - Track on Security and Privacy Unmanned aerial vehicles
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Title	Secure communication routing and attack detection in UAV networks using Gannet Walruses optimization algorithm and Sheppard Convolutional Spinal Network
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