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Online sequential attack detection for ADS-B data based on hierarchical temporal memory
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DOI：10.1016/j.cose.2019.101599.png)
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In the next generation air traffic surveillance, ADS-B is the primary surveillance method to improve situation awareness capabilities. But ADS-B protocol is absent of sufficient security considerations, especially for data integrity and authentication. As a result, attack patterns on ADS-B data are emerging and efficient attack detection strategies are in great demand to enhance data security. To decrease the time delay of detection, enhance accuracy and mitigate the concept drift impacts, the online sequential attack detection strategy based on hierarchical temporal memory are proposed. By applying binary encoding, ADS-B data is transformed into sparse distribution representation with temporal and spatial correlations. The encoded data is push into hierarchical temporal memory and online learning schemes are established for the ADS-B stream data. With the sequential ADS-B data, hierarchical temporal memory is updated and used to generate the deviations between predictions and original values for the corresponding ADS-B data. Designing and applying deviation analysis, sequential analysis and adaptive threshold check, the differences between normal and novelty are magnified and easy to be distinguished. According to experimental analysis, the attack detection strategy is efficient on processing time and accuracy. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Attack detection
Hierarchical temporal memory
Automatic dependent surveillance-broadcast
Air traffic surveillance
Novelty detection
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