SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast
This 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 de...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10443932/ |
| _version_ | 1797243348093763584 |
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| author | Nursah Cevik Sedat Akleylek |
| author_facet | Nursah Cevik Sedat Akleylek |
| author_sort | Nursah Cevik |
| collection | DOAJ |
| description | This 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. |
| first_indexed | 2024-04-24T18:53:41Z |
| format | Article |
| id | doaj.art-103d5d9785a64ca5bd9b7738057e313a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:53:41Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-103d5d9785a64ca5bd9b7738057e313a2024-03-26T17:46:16ZengIEEEIEEE Access2169-35362024-01-0112356433566210.1109/ACCESS.2024.336918110443932SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- BroadcastNursah Cevik0https://orcid.org/0000-0001-7066-3633Sedat Akleylek1https://orcid.org/0000-0001-7005-6489HAVELSAN, Ankara, TurkeyDepartment of Computer Engineering, Istinye University, İstanbul, TurkeyThis 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.https://ieeexplore.ieee.org/document/10443932/ADS-Banomaly based intrusion detection systemanomaly detection systemcyber securityavionics securitydeep learning |
| spellingShingle | Nursah Cevik Sedat Akleylek SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast IEEE Access ADS-B anomaly based intrusion detection system anomaly detection system cyber security avionics security deep learning |
| title | SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast |
| title_full | SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast |
| title_fullStr | SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast |
| title_full_unstemmed | SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast |
| title_short | SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast |
| title_sort | sok of machine learning and deep learning based anomaly detection methods for automatic dependent surveillance broadcast |
| topic | ADS-B anomaly based intrusion detection system anomaly detection system cyber security avionics security deep learning |
| url | https://ieeexplore.ieee.org/document/10443932/ |
| work_keys_str_mv | AT nursahcevik sokofmachinelearninganddeeplearningbasedanomalydetectionmethodsforautomaticdependentsurveillancebroadcast AT sedatakleylek sokofmachinelearninganddeeplearningbasedanomalydetectionmethodsforautomaticdependentsurveillancebroadcast |