Anomaly detection on flight route using similarity and grouping approach based-on automatic dependent surveillance-broadcast

(1) * Mohammad Yazdi Pusadan Mail (Department of Informatics, Institut Teknologi Sepuluh Nopember; Department of Informatics, Universitas Tadulako, Indonesia)
(2) Joko Lianto Buliali Mail (Department of Informatics, Institut Teknologi Sepuluh Nopember, Indonesia)
(3) Raden Venantius Hari Ginardi Mail (Department of Informatics, Institut Teknologi Sepuluh Nopember, Indonesia)
*corresponding author

Abstract


Flight anomaly detection is used to determine the abnormal state data on the flight route. This study focused on two groups: general aviation habits (C1)and anomalies (C2). Groups C1 and C2 are obtained through similarity test with references. The methods used are: 1) normalizing the training data form, 2) forming the training segment 3) calculating the log-likelihood value and determining the maximum log-likelihood (C1) and minimum log-likelihood (C2) values, 4) determining the percentage of data based on criteria C1 and C2 by grouping SVM, KNN, and K-means and 5) Testing with log-likelihood ratio. The results achieved in each segment are Log-likelihood value in C1Latitude is -15.97 and C1Longitude is -16.97. On the other hand, Log-likelihood value in C2Latitude is -19.3 (maximum) and -20.3 (minimum), and log-likelihood value in C2Longitude is -21.2 (maximum) and -24.8 (minimum). The largest percentage value in C1 is 96%, while the largest in C2 is 10%. Thus, the highest potential anomaly data is 10%, and the smallest is 3%. Also, there are performance tests based on F-measure to get accuracy and precision.

Keywords


Segment; Log-likelihood ratio; Grouping similarity; Accuracy; Anomaly detection

   

DOI

https://doi.org/10.26555/ijain.v5i3.232
      

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References


[1] L. Li, M. Gariel, R. J. Hansman, and R. Palacios, “Anomaly detection in onboard-recorded flight data using cluster analysis,” in Digital Avionics Systems Conference (DASC), 2011 IEEE/AIAA 30th, 2011, p. 4A4--1, doi: 10.1109/DASC.2011.6096068.

[2] L. Li, S. Das, R. John Hansman, R. Palacios, and A. N. Srivastava, “Analysis of Flight Data Using Clustering Techniques for Detecting Abnormal Operations,” J. Aerosp. Inf. Syst., vol. 12, no. 9, pp. 587–598, Sep. 2015, doi: 10.2514/1.I010329.

[3] P. Novianti, D. Setyorini, and U. Rafflesia, “K-Means cluster analysis in earthquake epicenter clustering,” Int. J. Adv. Intell. Informatics, vol. 3, no. 2, pp. 81–89, Jul. 2017, doi: 10.26555/ijain.v3i2.100.

[4] D. S. Hicok and D. Lee, “Application of ADS-B for airport surface surveillance,” in 17th DASC. AIAA/IEEE/SAE. Digital Avionics Systems Conference. Proceedings (Cat. No. 98CH36267), 1998, vol. 2, pp. F34--1, doi: 10.1109/DASC.1998.739823.

[5] X. Zhang, J. Zhang, S. Wu, Q. Cheng, and R. Zhu, “Aircraft monitoring by the fusion of satellite and ground ADS-B data,” Acta Astronaut., vol. 143, pp. 398–405, 2018, doi: 10.1016/j.actaastro.2017.11.026.

[6] M. Gariel, F. Kunzi, and R. J. Hansman, “An algorithm for conflict detection in dense traffic using ADS-B,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., pp. 1–12, 2011, doi: 10.1109/DASC.2011.6095916.

[7] M. Orefice, V. Di Vito, F. Corraro, G. Fasano, and D. Accardo, “Aircraft conflict detection based on ADS-B surveillance data,” 2014 IEEE Int. Work. Metrol. Aerospace, Metroaerosp. 2014 - Proc., pp. 277–282, 2014, doi: 10.1109/MetroAeroSpace.2014.6865934.

[8] G. Cheng and X. Tong, “Fuzzy Clustering Multiple Kernel Support Vector Machine,” 2018 Int. Conf. Wavelet Anal. Pattern Recognit., pp. 7–12, doi: 10.1109/ICWAPR.2018.8521307.

[9] R. Wang, W. Li, R. Li, and L. Zhang, “Signal Processing : Image Communication Automatic blur type classification via ensemble SVM,” Signal Process. Image Commun., vol. 71, no. 37, pp. 24–35, 2019, doi: 10.1016/j.image.2018.08.003.

[10] L. V Utkin, “An imprecise extension of SVM-based machine learning models,” Neurocomputing, 2018, doi: 10.1016/j.neucom.2018.11.053.

[11] Z. Liu, Z. Zhang, Y. Liu, J. Dezert, and Q. Pan, “Knowledge-Based Systems A new pattern classification improvement method with local quality matrix based on K-NN,” Knowledge-Based Syst., 2018, doi: 10.1016/j.knosys.2018.11.001.

[12] S. S. Aung, I. Nagayama, and S. Tamaki, “Regional Distance-based k-NN Classification,” pp. 56–62, 2017, doi: 10.1109/ICIIBMS.2017.8279719.

[13] K. Shankar and M. Ilayaraja, “Secure Optimal k -NN on Encrypted Cloud Data using Homomorphic Encryption with Query Users,” 2018 Int. Conf. Comput. Commun. Informatics, pp. 1–7, 2018, doi: 10.1109/ICCCI.2018.8441290.

[14] S. F. Hussain and M. Haris, “A k-means based Co-clustering (kCC) Algorithm for Sparse, High Dimensional Data,” Expert Syst. Appl., 2018, doi: 10.1016/j.eswa.2018.09.006.

[15] G. Tzortzis and A. Likas, “The MinMax k-Means clustering algorithm,” Pattern Recognit., vol. 47, no. 7, pp. 2505–2516, Jul. 2014, doi: 10.1016/j.patcog.2014.01.015.

[16] W. He, D. Zhao, Y. Zheng, and J. Xie, “An Expected Patch Log Likelihood Denoising Method Based on Internal and External Image Similarity,” in 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI), 2018, pp. 1–4, doi: 10.1109/ISSI.2018.8538103.

[17] V. M. Suhila and B. C. Kovoor, “Optimized H ybrid Approach for T opic S earch using Log Likelihood and RV Coefficient,” 2017 Int. Conf. Energy, Commun. Data Anal. Soft Comput., pp. 338–341, 2017, doi: 10.1109/ICECDS.2017.8390062.

[18] J. Lee and H. Chung, “Exact and approximate log-likelihood ratio of M-ary QAM with two-time dimensions,” ICT Express, vol. 5, no. 3, pp. 173–177, 2019, doi: 10.1016/j.icte.2018.08.004.

[19] Carl Laufer, The Hobbyist’s Guide to the RTL-SDR: Really Cheap Software Defined Radio. 2015, available at: Google Scholar.

[20] S. Theodoridis, A. Pikrakis, K. Koutroumbas, and D. Cavouras, Introduction to pattern recognition: a matlab approach. Academic Press, 2010, available at: Google Scholar.

[21] H. Hartono, O. S. Sitompul, T. Tulus, and E. B. Nababan, “Biased support vector machine and weighted-smote in handling class imbalance problem,” Int. J. Adv. Intell. Informatics, vol. 4, no. 1, p. 21, Mar. 2018, doi: 10.26555/ijain.v4i1.146.

[22] A. Prahara, A. Pranolo, and R. Drezewski, “GPU Accelerated Number Plate Localization in Crowded Situation,” Int. J. Adv. Intell. Informatics, vol. 1, no. 3, pp. 150–157, 2015, doi: 10.26555/ijain.v1i3.46.

[23] M. Latah and L. Toker, “A novel intelligent approach for detecting DoS flooding attacks in software-defined networks,” Int. J. Adv. Intell. Informatics, vol. 4, no. 1, pp. 11–20, Mar. 2018, doi: 10.26555/ijain.v4i1.138.

[24] T. Lakshmi Priya, N. R. Raajan, N. Raju, P. Preethi, and S. Mathini, “Speech and non-speech identification and classification using KNN algorithm,” Procedia Eng., vol. 38, pp. 952–958, 2012, doi: 10.1016/j.proeng.2012.06.120.

[25] G. Bhattacharya, K. Ghosh, and A. S. Chowdhury, “An affinity-based new local distance function and similarity measure for kNN algorithm,” Pattern Recognit. Lett., vol. 33, no. 3, pp. 356–363, 2012, doi: 10.1016/j.patrec.2011.10.021.

[26] Y. Ding, Y. Zhao, X. Shen, M. Musuvathi, and T. Mytkowicz, “Yinyang k-means: A drop-in replacement of the classic k-means with consistent speedup,” in 32nd International Conference on Machine Learning, ICML 2015, 2015, available at: Google Scholar.

[27] S. Ahmadian, A. Norouzi-Fard, O. Svensson, and J. Ward, “Better Guarantees for $k$-Means and Euclidean k-Median by Primal-Dual Algorithms,” SIAM J. Comput., pp. FOCS17-97-FOCS17-156, Oct. 2019, doi: 10.1137/18M1171321.

[28] X. Liu et al., “Multiple Kernel k-means with Incomplete Kernels,” IEEE Trans. Pattern Anal. Mach. Intell., pp. 1–1, 2019, doi: 10.1109/TPAMI.2019.2892416.

[29] W. Härdle and L. Simar, Applied Multivariate Statistical Analysis, 2015, no. 4, doi: 10.1198/tech.2005.s319.

[30] I. Kulikovskikh and S. Prokhorov, “Minimizing the effects of floor and ceiling to improve the convergence of log-likelihood,” Procedia Eng., vol. 201, pp. 779–788, 2017, doi: 10.1016/j.proeng.2017.09.627.

[31] D. Jarušková and V. I. Piterbarg, “Log-likelihood ratio test for detecting transient change,” Stat. Probab. Lett., vol. 81, no. 5, pp. 552–559, 2011, doi: 10.1016/j.spl.2011.01.006.




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