Sea turtle foraging algorithm with hybrid deep learning-based intrusion detection for the internet of drones environment

dc.creatorEscorcia-Gutierrez, José
dc.creatorGamarra, Margarita
dc.creatorLeal, Esmeide
dc.creatorMadera, Natasha
dc.creatorSoto, Carlos
dc.creatorMansour, Romany F.
dc.creatorAlharbi, Meshal
dc.creatorAlkhayyat, Ahmed
dc.creatorGupta, Deepak
dc.date2023-05-19T22:52:10Z
dc.date2025
dc.date2023-05-19T22:52:10Z
dc.date2023
dc.date.accessioned2023-10-03T19:58:50Z
dc.date.available2023-10-03T19:58:50Z
dc.identifierJosé Escorcia-Gutierrez, Margarita Gamarra, Esmeide Leal, Natasha Madera, Carlos Soto, Romany F. Mansour, Meshal Alharbi, Ahmed Alkhayyat, Deepak Gupta, Sea turtle foraging algorithm with hybrid deep learning-based intrusion detection for the internet of drones environment, Computers and Electrical Engineering, Volume 108, 2023, 108704, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2023.108704
dc.identifierhttps://hdl.handle.net/11323/10156
dc.identifier10.1016/j.compeleceng.2023.108704
dc.identifier0045-7906
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9173533
dc.descriptionThe Internet of Drones (IoD) allows for coordinated control of airspace for Unmanned Aerial Vehicles (UAVs), also known as drones. The decreasing costs of processors, sensors, and wireless connectivity have made it possible to use UAVs in many variety of military to civilian applications. While most applications utilizing the drones in the IoD have been real-time related, users are now interested in obtaining real-time services from drones that are tailored to a specific fly zone. This study develops a Sea Turtle Foraging Algorithm with Hybrid Deep Learning-based Intrusion Detection (STFA-HDLID) as a algorithm that recognizes and categorizes intrusions in the IoD environment. For this purpose, it is necessary to implement data pre-processing to standardize the input data via min-max normalization. Additionally, the feature selection process is also based on the STFA. Finally, a Deep Belief Network (DBN) with a Sparrow Search Optimization (SSO) algorithm is used for classification. A comprehensive experimental analysis is performed on a benchmark dataset to demonstrate the performance of the STFA-HDLID, which achieves maximum accuracy of 99.51% and 98.85% on the TON_IoT and UNSW-NB15 datasets, respectively, outperforming other techniques.
dc.format17 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier Ltd.
dc.publisherUnited Kingdom
dc.relationComputers and Electrical Engineering
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dc.rights© 2023 The Authors. Published by Elsevier Ltd.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S0045790623001283?via%3Dihub
dc.subjectIntrusion detection
dc.subjectDeep learning
dc.subjectInternet of drones
dc.subjectMetaheuristics
dc.subjectFeature selection
dc.titleSea turtle foraging algorithm with hybrid deep learning-based intrusion detection for the internet of drones environment
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_970fb48d4fbd8a85


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