Adaptive Threshold for Anomaly Detection in ATM Radar Data Streams
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-09282-4_36
Reference17 articles.
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2. Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K.: Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tut. 16(1), 303–336 (2014). https://doi.org/10.1109/SURV.2013.052213.00046
3. de Riberolles, T., Song, J., Zou, Y., Silvestre, G., Larrieu, N.: Characterizing radar network traffic: a first step towards spoofing attack detection. In: 2020 IEEE Aerospace Conference, pp. 1–8 (2020). https://doi.org/10.1109/AERO47225.2020.9172292
4. Esposito, C., Landrum, G.A., Schneider, N., Stiefl, N., Riniker, S.: GHOST: adjusting the decision threshold to handle imbalanced data in machine learning. J. Chem. Inf. Model. 61(6), 2623–2640 (2021). https://doi.org/10.1021/acs.jcim.1c00160
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