The Automatic Dependent Surveillance Broadcast(ADS-B)and the Secondary Surveillance Radar(SSR)are two important monitoring methods for air traffic control. ADS-B is the communication protocol currently being introduced, which will play an important role in the next generation of air traffic surveillance. However, the ADS-B protocol lacks security measures and is vulnerable to false data injection attacks. In order to identify the anomaly data in ADS-B, a set of sample data with multi-dimensional attribute features is obtained by using the SSR data synchronized with it and the covariance matrix obtained by Kalman filtering, using the method of support vector data field description(SVDD). By training the sample data, a classifier for detecting anomalies can be obtained. This classifier is used to detect the ADS-B data received later, thereby identifying abnormal data. The simulation experiment shows that the method has a correct recognition rate of more than 80% for ADS-B anomaly data, and the detection false alarm rate for fixed deviation injection is 5%, the false alarm rate is 0, and the false alarm is detected for random deviation injection. The rate is 5% and the missed alarm rate is 12.5%, which verifies the feasibility of the method.
WANG Zhenhao
,
WANG Buhong
. ADS-B anomaly data detection based on SVDD[J]. Journal of Hebei University(Natural Science Edition), 2019
, 39(3)
: 323
-329
.
DOI: 10.3969/j.issn.1000-1565.2019.03.015
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