Anomaly Detection on ADS-B Flight Data Using Machine Learning Techniques

Osman Taşdelen*, Levent Çarkacioglu, Behçet Uğur Töreyin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


With the rapid increase in the number of flights all over the world, the management and control of flight operations has become difficult in recent years. Moreover, the expectations for the aviation sector indicate that this increase will continue in the upcoming years. Therefore, safer and systematic monitoring systems by eliminating the requirement of human-dependent tracking during the air travel of an aircraft and automating the detection of abnormal situations has become a major problem in aviation sector. With the recent advances in artificial intelligence, a safer and systematic tracking system for controlling the airspace by eliminating the need for human-dependent tracking during the flight of aircraft in the air has become possible. In this study, we aimed to create a system that detects and predicts movements to indicate abnormal, dangerous situations in the airspace by monitoring radar flight data using machine learning and deep learning techniques. We applied two different methods, i.e., Proximity Based kNN and Auto Encoder We used real-life historical radar flight data set which consists of Flight Radar 24 data were converted from ADS-B messages for learning. We created simulation data and used this data for testing and validation for our trained model. Within the scope of this project, we also developed a system to monitor air traffic through radar tracks with our model and present the abnormal situations to the user through a visual interface for decision support. In this visualization, we present the abnormal situations if one of the algorithms labeled as anomaly. Results for both methods have shown that our findings were similar to the real-life predictions.

Original languageEnglish
Title of host publicationComputational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings
EditorsNgoc Thanh Nguyen, Ngoc Thanh Nguyen, Lazaros Iliadis, Ilias Maglogiannis, Bogdan Trawiński
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030880804
Publication statusPublished - 2021
Event13th International Conference on Computational Collective Intelligence, ICCCI 2021 - Virtual, Online
Duration: 29 Sept 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12876 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Computational Collective Intelligence, ICCCI 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • ADS-B
  • Air traffic management
  • Anomaly detection
  • Auto encoder
  • Deep learning
  • Flight control
  • Machine learning
  • Proximity based kNN


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