Near-Real-Time IDS for the U.S. FAA’s NextGen ADS-B
1
School of Computing, University of South Alabama, Mobile, AL 36688, USA
2
Department of Computer Science, University of West Florida, Pensacola, FL 32514, USA
3
Department of Computer Science, Sam Houston State University, Hunstville, TX 77340, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Isaac Triguero and Min Chen
Big Data Cogn. Comput. 2021, 5(2), 27; https://doi.org/10.3390/bdcc5020027
Received: 8 May 2021
/
Revised: 31 May 2021
/
Accepted: 3 June 2021
/
Published: 16 June 2021
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.
View Full-Text
Keywords:
Next Generation (NextGen) Air Transportation Systems; Automatic Dependent Surveillance-Broadcast (ADS-B); Intrusion Detection System (IDS); network attack signatures; data mining process; Support Vector Machine (SVM); big data
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Mink, D.M.; McDonald, J.; Bagui, S.; Glisson, W.B.; Shropshire, J.; Benton, R.; Russ, S. Near-Real-Time IDS for the U.S. FAA’s NextGen ADS-B. Big Data Cogn. Comput. 2021, 5, 27. https://doi.org/10.3390/bdcc5020027
AMA Style
Mink DM, McDonald J, Bagui S, Glisson WB, Shropshire J, Benton R, Russ S. Near-Real-Time IDS for the U.S. FAA’s NextGen ADS-B. Big Data and Cognitive Computing. 2021; 5(2):27. https://doi.org/10.3390/bdcc5020027
Chicago/Turabian StyleMink, Dustin M., Jeffrey McDonald, Sikha Bagui, William B. Glisson, Jordan Shropshire, Ryan Benton, and Samuel Russ. 2021. "Near-Real-Time IDS for the U.S. FAA’s NextGen ADS-B" Big Data and Cognitive Computing 5, no. 2: 27. https://doi.org/10.3390/bdcc5020027
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
