Research Article
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Classification of Attacks on ADS-B Devices with Artificial Learning

Year 2024, , 38 - 47, 11.06.2024
https://doi.org/10.54525/bbmd.1454512

Abstract

From the beginning of air transport, the tracking of aircraft is crucial for the safety of the flight and the management of air traffic. In the follow-up of aircraft, there are airspace operators and institutions that manage the area where the aircraft is located. Institutions controlling this airspace use various systems to track aircraft. All of these systems constitute Air Traffic Management Systems. There are many types of radars used to detect aircraft. Apart from these radars, Automatic Dependent Surveillance-Broadcast (ADS-B) devices are used to detect the position of aircraft. ADS-B devices are preferred by attackers as they are cheaper to install and cost than other radar systems.
Within the scope of this study, possible attacks were examined through a publication obtained from aircraft simulation tools for the classification of attacks on ADS-B data. A system model has been proposed to detect the possible attacks on a obtained data set. Specifically, after the preprocessing applied on a dataset, the evaluations are performed with different artificial learning techniques. These techniques include Support Vector Machine, Decision Tree and Naive Bayes classifier machine learning techniques. The tests are evaluated with accuracy, precision, sensitivity and F-criteria.

References

  • Khandker, S., Turtiainen, H. Costin A. ve Hämäläinen T., On the (In)Security of 1090ES and UAT978 Mobile Cockpit Information Systems–An Attacker Perspective on the Availability of ADS-B Safety- and Mission-Critical Systems, in IEEE Access, vol. 10, pp. 37718-37730, 2022, doi: 10.1109/ACCESS.2022.3164704.
  • Li, T., Wang B., Shang, F., Tian, J., Cao, K., Online sequential attack detection for ADS-B data based on hierarchical temporal memory, Computers & Security, vol. 87 (2019) 101599.
  • Li, T., Wang, B., Sequential collaborative detection strategy on ADS-B data attack, International Journal of Critical Infrastructure Protection, vol. 24 (2019), pp. 78-99.
  • Asari, A., Alagheband, M. R., Bayat, M., Asaar, M.R., A new provable hierarchical anonymous certificateless authentication protocol with aggregate verification in ADS-B systems, Computer Networks, vol. 185 (2021), 107599.
  • Luo, P., Wang, B., Li, T., Tian, J., ADS-B anomaly data detection model based on VAE-SVDD, Computers & Security, vol. 104 (2021), 102213.
  • TajDini, M., Sokolov V., Skladannyi, P., Performing Sniffing and Spoofing Attack Against ADS-B and Mode S using Software Define Radio, in 2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo), IEEE, 2021, pp. 1–5.
  • El Marady, A. A. W., Enhancing accuracy and security of ADS-B via MLAT assisted-flight information system, in 2017 12th International Conference on Computer Engineering and Systems (ICCES), IEEE, 2017, pp. 182–187.
  • Wahlgren, A., Thorn, J., Detecting ADS-B spoofing attacks: using collected and simulated data, 2021.
  • Khan, S., Thorn, J., Wahlgren, A., Gurtov, A., Intrusion detection in automatic dependent surveillance-broadcast (ADS-B) with machine learning, in 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), IEEE, 2021, pp. 1–10.
  • Kacem, T., Kaya, A., Keceli, A. S., Catal, C., Wijsekera, D., Costa, P., ADS-B Attack Classification using Machine Learning Techniques, in 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), IEEE, 2021, pp. 7–12.
  • Li, N., Lin, L., Li, F., ADS-B Anomaly Data Detection Using SVDD-based LSTM Encoder-Decoder Algorithm, in 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), IEEE, 2021, pp. 1295–1300.
  • Damis, H. A., Shehada, D., Fachkha, C., Gawanmeh, A., Al-Karaki, J. N., A microservices architecture for ADS-B data security using blockchain, in 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS), IEEE, 2020, pp. 1–4.
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  • Yiğidim, H. A., Makine Öğrenme Algoritmalarını Kullanarak Ağ Trafiğinin Sınıflandırılması, Master’s Thesis, TOBB Ekonomi ve Teknoloji Üniversitesi Fen Bilimleri Enstitüsü, 2012. Berwick, R., An Idiot’s guide to Support vector machines (SVMs).
  • Support Vector Machine Explained-Theory, Implementation, and Visualization, https://www.linkedin.com/pulse/support-vector-machine-explained-theory-visualization-zixuan-zhang (Erişim Tarihi: 02.04. 2023).
  • Naive Bayes Classifier Tutorial: with Python Scikit-learn, https://www.datacamp.com/tutorial/naive-bayes-scikit-learn (Erişim Tarihi: 02.04. 2023).
  • Shoba R., Kenta N., Christian S., Micheal G., Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Amsterdam: ELSEVIER Yayınları, 2019, 405, books.google.com.tr [Erişim Tarihi: 29.03.2023].
  • Naive Bayes Classifiers, GeeksforGeeks, Mar. 03, 2017. https://www.geeksforgeeks.org/naive-bayes-classifiers/ (Erişim Tarihi: 02.04. 2023).
  • Aksu G., Dogan, N., Comparison of Decision Trees Used in Data Mining”, Pegem J. Educ. Instr., vol. 9, no. 4, pp. 1183–1208, 2019.

ADS-B Cihazlarına Yapılan Saldırıların Yapay Öğrenme ile Sınıflandırılması

Year 2024, , 38 - 47, 11.06.2024
https://doi.org/10.54525/bbmd.1454512

Abstract

Havayolu taşımacılığı, başlangıcından itibaren hava araçlarının takibi, uçuşun emniyeti ve hava trafiğinin yönetimi için oldukça önemlidir. Hava taşıtlarının takibinde ise hava taşıtının konumumun bulunduğu alanı yöneten hava sahası işletmecileri, kurumları bulunmaktadır. Bu hava sahasını kontrol eden kurumlar hava taşıtlarını takip edebilmek için çeşitli sistemler kullanmaktadır. Bu sistemler bütününü hava trafik yönetim sistemleri oluşturmaktadır. Hava araçlarının algılanması için kullanılan birçok radar çeşidi bulunmaktadır. Bu radarların dışında hava taşıtlarının konumunu saptamak için Otomatik Bağımlı Gözetim Yayını (Automatic Dependent Surveillance-Broadcast (ADS-B)) cihazları kullanılmaktadır. ADS-B cihazları kurulumu ve maliyeti diğer radar sistemlerine göre daha ucuz olduğundan saldırganlar için daha çok tercih edilir. Bu çalışma kapsamında, ADS-B cihazlarının verisine yapılan saldırıların sınıflandırılması için hava taşıtı simülasyon araçlarından elde edilen bir yayın üzerinden olası saldırılar incelenmiştir. Elde edilen özgün bir veri kümesi üzerinden olası saldırıların saptanması amacıyla bir sistem modeli önerilmiştir. Amaca uygun olarak, veri kümesinde uygulanan ön işlemler sonrasında, farklı yapay öğrenme teknikleri ile değerlendirmeler yapılmıştır. Bu teknikler, Destek Vektör Makineleri (SVM), İkili Karar Ağacı ve Naive Bayes sınıflandırıcısı makine öğrenme tekniklerini içermektedir. Yapılan sınamalar, doğruluk, tutturma, duyarlılık ve F-ölçüsü ile değerlendirilmiştir.

References

  • Khandker, S., Turtiainen, H. Costin A. ve Hämäläinen T., On the (In)Security of 1090ES and UAT978 Mobile Cockpit Information Systems–An Attacker Perspective on the Availability of ADS-B Safety- and Mission-Critical Systems, in IEEE Access, vol. 10, pp. 37718-37730, 2022, doi: 10.1109/ACCESS.2022.3164704.
  • Li, T., Wang B., Shang, F., Tian, J., Cao, K., Online sequential attack detection for ADS-B data based on hierarchical temporal memory, Computers & Security, vol. 87 (2019) 101599.
  • Li, T., Wang, B., Sequential collaborative detection strategy on ADS-B data attack, International Journal of Critical Infrastructure Protection, vol. 24 (2019), pp. 78-99.
  • Asari, A., Alagheband, M. R., Bayat, M., Asaar, M.R., A new provable hierarchical anonymous certificateless authentication protocol with aggregate verification in ADS-B systems, Computer Networks, vol. 185 (2021), 107599.
  • Luo, P., Wang, B., Li, T., Tian, J., ADS-B anomaly data detection model based on VAE-SVDD, Computers & Security, vol. 104 (2021), 102213.
  • TajDini, M., Sokolov V., Skladannyi, P., Performing Sniffing and Spoofing Attack Against ADS-B and Mode S using Software Define Radio, in 2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo), IEEE, 2021, pp. 1–5.
  • El Marady, A. A. W., Enhancing accuracy and security of ADS-B via MLAT assisted-flight information system, in 2017 12th International Conference on Computer Engineering and Systems (ICCES), IEEE, 2017, pp. 182–187.
  • Wahlgren, A., Thorn, J., Detecting ADS-B spoofing attacks: using collected and simulated data, 2021.
  • Khan, S., Thorn, J., Wahlgren, A., Gurtov, A., Intrusion detection in automatic dependent surveillance-broadcast (ADS-B) with machine learning, in 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), IEEE, 2021, pp. 1–10.
  • Kacem, T., Kaya, A., Keceli, A. S., Catal, C., Wijsekera, D., Costa, P., ADS-B Attack Classification using Machine Learning Techniques, in 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), IEEE, 2021, pp. 7–12.
  • Li, N., Lin, L., Li, F., ADS-B Anomaly Data Detection Using SVDD-based LSTM Encoder-Decoder Algorithm, in 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), IEEE, 2021, pp. 1295–1300.
  • Damis, H. A., Shehada, D., Fachkha, C., Gawanmeh, A., Al-Karaki, J. N., A microservices architecture for ADS-B data security using blockchain, in 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS), IEEE, 2020, pp. 1–4.
  • Shang, F., Wang, B., Li, T., Tian, J., Cao, K., Guo, R., Adversarial examples on deep-learning-based ADS-B spoofing detection, IEEE Wirel. Commun. Lett., vol. 9, no. 10, pp. 1734–1737, 2020. SHGM, Mode-S Tahsis İşlemleri | Sivil Havacılık Genel Müdürlüğü. http://172.16.10.52:81/tr/hava-araci-islemleri/2233-mode-s-tahsis-islemleri (Erişim Tarihi: 02.04. 2023).
  • Manikanth, What is the use of data standardization and where do we use it in machine learning, Analytics Vidhya, Mar. 19, 2021. https://medium.com/analytics-vidhya/what-is-the-use-of-data-standardization-and-where-do-we-use-it-in-machine-learning-97b71a294e24 (Erişim Tarihi: 02.04. 2023).
  • Nguyen, T. T., Armitage, G., A survey of techniques for internet traffic classification using machine learning, IEEE Commun. Surv. Tutor., vol. 10, no. 4, pp. 56–76, 2008.
  • Deshmukh, D. H., Ghorpade, T., Padiya, P., Improving classification using preprocessing and machine learning algorithms on NSL-KDD dataset, in 2015 International Conference on Communication, Information & Computing Technology (ICCICT), IEEE, 2015, pp. 1–6.
  • Yiğidim, H. A., Makine Öğrenme Algoritmalarını Kullanarak Ağ Trafiğinin Sınıflandırılması, Master’s Thesis, TOBB Ekonomi ve Teknoloji Üniversitesi Fen Bilimleri Enstitüsü, 2012. Berwick, R., An Idiot’s guide to Support vector machines (SVMs).
  • Support Vector Machine Explained-Theory, Implementation, and Visualization, https://www.linkedin.com/pulse/support-vector-machine-explained-theory-visualization-zixuan-zhang (Erişim Tarihi: 02.04. 2023).
  • Naive Bayes Classifier Tutorial: with Python Scikit-learn, https://www.datacamp.com/tutorial/naive-bayes-scikit-learn (Erişim Tarihi: 02.04. 2023).
  • Shoba R., Kenta N., Christian S., Micheal G., Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Amsterdam: ELSEVIER Yayınları, 2019, 405, books.google.com.tr [Erişim Tarihi: 29.03.2023].
  • Naive Bayes Classifiers, GeeksforGeeks, Mar. 03, 2017. https://www.geeksforgeeks.org/naive-bayes-classifiers/ (Erişim Tarihi: 02.04. 2023).
  • Aksu G., Dogan, N., Comparison of Decision Trees Used in Data Mining”, Pegem J. Educ. Instr., vol. 9, no. 4, pp. 1183–1208, 2019.
There are 22 citations in total.

Details

Primary Language Turkish
Subjects Information Systems (Other)
Journal Section Research Articles
Authors

İbrahim Meral 0000-0002-5053-8613

Elif Bozkaya 0000-0001-6960-2585

Early Pub Date March 18, 2024
Publication Date June 11, 2024
Published in Issue Year 2024

Cite

IEEE İ. Meral and E. Bozkaya, “ADS-B Cihazlarına Yapılan Saldırıların Yapay Öğrenme ile Sınıflandırılması”, bbmd, vol. 17, no. 1, pp. 38–47, 2024, doi: 10.54525/bbmd.1454512.