Malicious ADS-B data Generation Based on Improved GAN

Jin, Lei; Jiang, Renhe; Wu, Zhijun

doi:10.1109/bigdatasecurity-hpsc-ids58521.2023.00023

2023 IEEE 9th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Sma 2023

DOI: 10.1109/bigdatasecurity-hpsc-ids58521.2023.00023

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Malicious ADS-B data Generation Based on Improved GAN

Lei Jin

Renhe Jiang

Zhijun Wu

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2024

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

Çevik,

Akleylek

2024

IEEE Access

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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. Anomaly detection methods based on machine learning and deep learning algorithms among proposed countermeasures against vulnerabilities of ADS-B are 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 in 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 focused on studies that use machine learning and deep learning algorithms for ADS-B security. In this context; this paper addresses research on this topic from different perspectives, and draws a road map for future research, and searches for five research questions related to machine learning and deep learning algorithms used on anomaly detection systems.

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

Çevik,

Akleylek

2024

IEEE Access

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Malicious ADS-B data Generation Based on Improved GAN

Cited by 1 publication

References 6 publications

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

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

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