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


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.


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



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