Anomaly detection system for ADS-B data: Attack vectors and machine learning models

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dc.authorscopusidSedat Akleylek / 15833929800
dc.authorwosidSedat Akleylek / N-2620-2019
dc.contributor.authorÇevik, Nurşah
dc.contributor.authorAkleylek, Sedat
dc.date.accessioned2025-04-18T09:57:37Z
dc.date.available2025-04-18T09:57:37Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe topic of security challenges and solutions for Automatic Dependent Surveillance-Broadcast (ADS-B) systems is becoming more critical day-to-day because of the increasing air traffic volume. Since aircraft and ground stations receive broadcast ADS-B data cannot check the source and integrity of data, ADS-B systems can be spoofed easily by transmitting false data. In this paper, we develop an anomaly detection system for ADS-B data as a security solution. Various parameter sets were analyzed to identify critical ones. We created attack vectors for eight different attack scenarios, such as spoofing, message injection, and virtual trajectory change attacks, and created hybrid datasets by combining different attack vectors to increase the detection ability of different attack scenarios. These datasets have covered a wide range of attack scenarios to increase the robustness of anomaly detection assessments. We used attack datasets to evaluate the performance of different ML and DL models. The random forest classifier and the extra tree classifier are the standout performers, both achieving an impressive accuracy of 0.999. The decision tree classifier, with an accuracy of 0.992, also demonstrates strong performance, though slightly below that of the random forest and extra tree models. The results of the decision tree classifier have the lowest false negative and false positive rate, which are 0.004 and 0, respectively. Among the deep learning models, the multilayer perceptron model achieves notable success with an accuracy of 0.981744. Based on the results of our model, we increase the accuracy and reliability compared to existing methods. Additionally, we share our datasets to encourage further research and enable other researchers to expand our findings. © 2024 Elsevier B.V.
dc.description.sponsorshipSedat Akleylek was partially supported by COST Action CA22168 .
dc.identifier.citationÇevik, N., & Akleylek, S. (2025). Anomaly detection system for ADS-B data: Attack vectors and machine learning models. Internet of Things, 29, 101416.
dc.identifier.doi10.1016/j.iot.2024.101416
dc.identifier.issn25426605
dc.identifier.scopus2-s2.0-85210039274
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1016/j.iot.2024.101416
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6901
dc.identifier.volume29
dc.indekslendigikaynakScopus
dc.institutionauthorAkleylek, Sedat
dc.institutionauthoridSedat Akleylek / 0000-0001-7005-6489
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofInternet of Things (The Netherlands)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - 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.subjectMachine Learning
dc.subjectIntrusion Detection System
dc.titleAnomaly detection system for ADS-B data: Attack vectors and machine learning models
dc.typeArticle

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