Comparison of machine learning based anomaly detection methods for ADS-B system

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dc.authorscopusidSedat Akyelek / 15833929800
dc.contributor.authorÇevik, Nurşah
dc.contributor.authorAkleylek, Sedat
dc.date.accessioned2025-04-18T06:23:09Z
dc.date.available2025-04-18T06:23:09Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThis paper introduces an anomaly/intrusion detection system utilizing machine learning techniques for detecting attacks in the Automatic Detection System-Broadcast (ADS-B). Real ADS-B messages between Türkiye's coordinates are collected to train and test machine learning models. After data collection and pre-processing steps, the authors generate the attack datasets by using real ADS-B data to simulate two attack scenarios, which are constant velocity in-crease/decrease and gradually velocity increase or decrease attacks. The efficacy of five machine learning algorithms, including decision trees, extra trees, gaussian naive bayes, k-nearest neighbors, and logistic regression, is evaluated across different attack types. This paper demonstrates that tree-based algorithms consistently exhibit superior performance across a spectrum of attack scenarios. Moreover, the research underscores the significance of anomaly or intrusion detection mechanisms for ADS-B systems, highlights the practical viability of employing tree-based algorithms in air traffic management, and suggests avenues for enhancing safety protocols and mitigating potential risks in the airspace domain.
dc.identifier.citationÇevik, N., & Akleylek, S. (2024, April). Comparison of Machine Learning Based Anomaly Detection Methods for ADS-B System. In International Conference on Information Technologies and Their Applications (pp. 275-286). Cham: Springer Nature Switzerland.
dc.identifier.doi10.1007/978-3-031-73420-5_23
dc.identifier.endpage286
dc.identifier.isbn978-303173419-9
dc.identifier.issn18650929
dc.identifier.scopus2-s2.0-85207843987
dc.identifier.scopusqualityQ3
dc.identifier.startpage275
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-031-73420-5_23
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6328
dc.identifier.volume2226
dc.indekslendigikaynakScopus
dc.institutionauthorAkleylek, Sedat
dc.institutionauthoridSedat Akyelek / 0000-0001-7005-6489
dc.language.isoen
dc.publisherSpringer science and business media deutschland GmbH
dc.relation.ispartofCommunications in computer and information science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectADS-B
dc.subjectAnomaly Detection System
dc.subjectAvionics Security
dc.subjectCyber Security
dc.subjectIDS
dc.subjectIntrusion Detection System
dc.subjectMachine Learning
dc.titleComparison of machine learning based anomaly detection methods for ADS-B system
dc.typeOther

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