Modern-day aircraft are flying computer networks, vulnerable to ground station flooding, ghost aircraft injection or flooding, aircraft disappearance, virtual trajectory modifications or false alarm attacks, and aircraft spoofing. This work lays out a data mining process, in the context of big data, to determine flight patterns, including patterns for possible attacks, in the U.S. National Air Space (NAS). Flights outside the flight patterns are possible attacks. For this study, OpenSky was used as the data source of Automatic Dependent Surveillance-Broadcast (ADS-B) messages, NiFi was used for data management, Elasticsearch was used as the log analyzer, Kibana was used to visualize the data for feature selection, and Support Vector Machine (SVM) was used for classification. This research provides a solution for attack mitigation by packaging a machine learning algorithm, SVM, into an intrusion detection system and calculating the feasibility of processing US ADS-B messages in near real time. Results of this work show that ADS-B network attacks can be detected using network attack signatures, and volume and velocity calculations show that ADS-B messages are processable at the scale of the U.S. Next Generation (NextGen) Air Traffic Systems using commodity hardware, facilitating real time attack detection. Precision and recall close to 80% were obtained using SVM.
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