A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

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dc.authoridDash, Ranjan Kumar/0000-0003-3482-465X
dc.authoridKonecki, Mario/0009-0009-7020-917X
dc.authoridIvkovic, Nikola/0000-0003-1730-2518
dc.authorwosidDash, Ranjan Kumar/ABA-8593-2020
dc.contributor.authorCengiz, Korhan
dc.contributor.authorLipsa, Swati
dc.contributor.authorDash, Ranjan Kumar
dc.contributor.authorIvkovic, Nikola
dc.contributor.authorKonecki, Mario
dc.date.accessioned2024-05-19T14:39:09Z
dc.date.available2024-05-19T14:39:09Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractUnmanned aerial vehicles (UAVs) are increasingly being deployed in crucial missions for the armed forces, law enforcement, industrial control monitoring, and other sectors. However, these hostile operating circumstances, along with the UAVs' dependence on wireless protocols, pose substantial security threats, limiting their mainstream application. With network security being such a major issue for UAV networks, the machine learning-based intrusion detection system (IDS) has been determined to be an effective strategy for protecting them. Additionally, though the existing methods offer effective strategies for detecting and categorizing abnormalities in the system, they are limited by their inability to adjust to various attack patterns. The dataset used as well as the memory and computational requirement of existing models, poses new challenges. One of the main concerns pertains to the reduced computational and memory demands of these models. So, the work carried out in this paper addresses this challenge. A new dimensional reduction technique based on correlation coefficient, information gain, and principal component analysis (PCA) is introduced to reduce the dimensionality of the UAV Attack Dataset. A novel intrusion detection system based on an artificial neural network (ANN) and genetic algorithm (GA) is then proposed. The genetic algorithm is used to generate the optimal weights of the artificial neural network. A comparison is made between the proposed model and the backpropagation network and its variant in terms of its convergence and prediction accuracy. Furthermore, the performance of the proposed model is compared with that of other classifiers. This comparison reveals that the proposed model is time efficient with an increased prediction accuracy of at least 6% more than other classifiers.en_US
dc.identifier.doi10.1109/ACCESS.2024.3349469
dc.identifier.endpage4937en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85181559420en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage4925en_US
dc.identifier.urihttps://doi.org10.1109/ACCESS.2024.3349469
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4713
dc.identifier.volume12en_US
dc.identifier.wosWOS:001142668100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectArtificial Neural Networken_US
dc.subjectBackpropagationen_US
dc.subjectGenetic Algorithmen_US
dc.subjectInformation Gainen_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectPearson Correlation Coefficienten_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectSparsityen_US
dc.subjectUnmanned Aerial Vehiclesen_US
dc.titleA Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communicationen_US
dc.typeArticleen_US

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