Evaluation of Machine Learning Models for Attack Detection in Unmanned Aerial Vehicle Networks

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dc.authoridhttps://orcid.org/0009-0003-0554-8163
dc.authoridhttps://orcid.org/0000-0002-1417-4461
dc.authoridhttps://orcid.org/0000-0002-0963-825X
dc.authoridhttps://orcid.org/0000-0001-9859-1600
dc.contributor.authorGörmüş, Ahmet Faruk
dc.contributor.authorGönen, Serkan
dc.contributor.authorHaşiloğlu, Abdulsamet
dc.contributor.authorYılmaz, Ercan Nurcan
dc.date.accessioned2025-06-26T07:39:58Z
dc.date.available2025-06-26T07:39:58Z
dc.date.issued2024
dc.departmentMühendislik ve Mimarlık Fakültesi
dc.description.abstractNowadays, unmanned aerial vehicles (UAVs) are increasingly utilized in various civil and military applications, highlighting the growing need for robust security in UAV networks. Cyberattacks on these networks can lead to operational disruptions and the loss of critical information. This study evaluates five machine learning models—Random Forest (RF), CatBoost, XGBoost, AdaBoost, and Artificial Neural Networks (ANN)—for detecting attacks on UAV networks using the CICIOT2023 (Canadian Institute for Cybersecurity Internet of Things 2023) dataset. Performance metrics such as accuracy, precision, sensitivity, and F1 score were used to assess these models. Among them, CatBoost demonstrated superior performance, achieving the highest accuracy and the fastest prediction time of 6.487 seconds, making it particularly advantageous for real-time attack detection. This study underscores the effectiveness of CatBoost in both accuracy and efficiency, positioning it as an ideal choice for enhancing UAV network security. The findings contribute to addressing cybersecurity vulnerabilities in UAV networks and support the development of more secure network infrastructures.
dc.identifier.citationGORMUS, A. F., GONEN, S., HASILOGLU, A., YILMAZ, E. N. (2024). Evaluation of Machine Learning Models for Attack Detection in Unmanned Aerial Vehicle Networks. Turkish Journal of Mathematics and Computer Science, 16(2),400-410. doi.org/10.47000/tjmcs.1568820
dc.identifier.doi10.47000/tjmcs.1568820
dc.identifier.endpage410
dc.identifier.issn2148-1830
dc.identifier.issue2
dc.identifier.startpage400
dc.identifier.urihttps://hdl.handle.net/11363/10006
dc.identifier.volume16
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorGörmüş, Ahmet Faruk
dc.institutionauthorGönen, Serkan
dc.institutionauthorHaşiloğlu, Abdulsamet
dc.institutionauthoridhttps://orcid.org/0009-0003-0554-8163
dc.institutionauthoridhttps://orcid.org/0000-0002-1417-4461
dc.institutionauthoridhttps://orcid.org/0000-0002-0963-825X
dc.language.isoen
dc.publisherMatematikçiler Derneği
dc.relation.ispartofTurkish Journal of Mathematics and Computer Science
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectUAV networks
dc.subjectcyber attack
dc.subjectattack detection
dc.subjectCICIOT2023 dataset
dc.subjectperformance evaluation
dc.subjectsecurity vulnerabilities
dc.titleEvaluation of Machine Learning Models for Attack Detection in Unmanned Aerial Vehicle Networks
dc.typeArticle

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