Autors: Babanova, M. B., Furnadzhiev, R. S., Shopov, M. P.
Title: Anomaly Detection in ADS-B Air Traffic Data Using Distributed Machine Learning †
Keywords: anomaly detection, Automatic Dependent Surveillance–Broadcast, Gaussian Mixture Model, Isolation Forest, machine learning, unsupervised learning

Abstract: This paper presents a comparative study of two unsupervised machine learning methods, Isolation Forest (IF) and Gaussian Mixture Model (GMM), for anomaly detection in Automatic Dependent Surveillance–Broadcast (ADS-B) data. While ADS-B provides critical flight telemetry, its high volume and inherent noise pose significant challenges for automated surveillance. To address this, we developed a distributed Extract Transform Load (ETL) pipeline using Apache Spark to analyze a dataset of 4.82 billion records. The results show a low Pearson correlation between the two models, suggesting that they capture complementary types of anomalies: IF identifies structural outliers through recursive partitioning, while GMM detects statistical deviations from learned probability densities. This study demonstrates that an ensemble of lightweight methods can effectively scale to billion-record datasets, with IF offering superior computational efficiency (∼1.7 h) compared to GMM (∼4.8 h). These results provide a robust baseline for developing scalable, real-time aviation monitoring systems to enhance global air traffic safety.

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Issue

Engineering Proceedings, vol. 150, 2026, Albania, https://doi.org/10.3390/engproc2026150024

Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus