SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast

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dc.authoridAkleylek, Sedat/0000-0001-7005-6489
dc.authorwosidAkleylek, Sedat/N-2620-2019
dc.contributor.authorCevik, Nursah
dc.contributor.authorAkleylek, Sedat
dc.date.accessioned2024-05-19T14:40:44Z
dc.date.available2024-05-19T14:40:44Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis paper focuses on the vulnerabilities of ADS-B, one of the avionics systems, and the countermeasures taken against these vulnerabilities proposed in the literature. Among the proposed countermeasures against the vulnerabilities of ADS-B, anomaly detection methods based on machine learning and deep learning algorithms were analyzed in detail. The advantages and disadvantages of using an anomaly detection system on ADS-B data are investigated. Thanks to advances in machine learning and deep learning over the last decade, it has become more appropriate to use anomaly detection systems to detect anomalies in ADS-B systems. To the best of our knowledge, this is the first survey to focus on studies using machine learning and deep learning algorithms for ADS-B security. In this context, this study addresses research on this topic from different perspectives, draws a road map for future research, and searches for five research questions related to machine learning and deep learning algorithms used in anomaly detection systems.en_US
dc.identifier.doi10.1109/ACCESS.2024.3369181
dc.identifier.endpage35662en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85186106717en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage35643en_US
dc.identifier.urihttps://doi.org10.1109/ACCESS.2024.3369181
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5007
dc.identifier.volume12en_US
dc.identifier.wosWOS:001184766900001en_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.subjectDeep Learningen_US
dc.subjectAircraften_US
dc.subjectAnomaly Detectionen_US
dc.subjectSurveysen_US
dc.subjectIntrusion Detectionen_US
dc.subjectDatabasesen_US
dc.subjectComputer Securityen_US
dc.subjectMachine Learningen_US
dc.subjectAds-Ben_US
dc.subjectAnomaly Based Intrusion Detection Systemen_US
dc.subjectAnomaly Detection Systemen_US
dc.subjectCyber Securityen_US
dc.subjectAvionics Securityen_US
dc.subjectDeep Learningen_US
dc.subjectIdsen_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectMachine Learningen_US
dc.titleSoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcasten_US
dc.typeArticleen_US

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