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The Automatic Dependent Surveillance Broadcast (ADS-B) system is a critical communication and surveillance technology used in the Next Generation (NextGen) project as it improves the accuracy and efficiency of air navigation. These systems allow air traffic controllers to have more precise and real-time information on the location and movement of aircraft, leading to increased safety and improved efficiency in the airspace. While ADS-B has been made mandatory for all aircraft in the Federal Aviation Administration (FAA) monitored airspace, its lack of security measures leaves it vulnerable to cybersecurity threats. Particularly, ADS-B signals are susceptible to false data injection attacks due to the lack of authentication and integrity measures, which poses a serious threat to the safety of the National Airspace System (NAS). Many studies have attempted to address these vulnerabilities; however, machine learning and deep learning approaches have gained significant interest due to their ability to enhance security without modifying the existing infrastructure. This paper investigates the use of Recurrent Neural Networks for detecting injection attacks in ADS-B data, leveraging the time-dependent nature of the data. The paper reviews previous studies that used different machine learning and deep learning techniques and presents the potential benefits of using RNN algorithms to improve ADS-B security.
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CYBER-PHYSICAL SECURITY OF AIR TRAFFIC SURVEILLANCE SYSTEMS
Cyber-physical system security is a significant concern in the critical infrastructure. Strong interdependencies between cyber and physical components render cyber-physical systems highly susceptible to integrity attacks such as injecting malicious data and projecting fake sensor measurements. Traditional security models partition cyber-physical systems into just two domains – high and low. This absolute partitioning is not well suited to cyber-physical systems because they comprise multiple overlapping partitions. Information flow properties, which model how inputs to a system affect its outputs across security partitions, are important considerations in cyber-physical systems. Information flows support traceability analysis that helps detect vulnerabilities and anomalous sources, contributing to the implementation of mitigation measures. This chapter describes an automated model with graph-based information flow traversal for identifying information flow paths in the Automatic Dependent Surveillance-Broadcast (ADS-B) system used in civilian aviation, and subsequently partitioning the flows into security domains. The results help identify ADS-B system vulnerabilities to failures and attacks, and determine potential mitigation measures.
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- Award ID(s):
- 1837472
- PAR ID:
- 10189011
- Date Published:
- Journal Name:
- Critical Infrastructure Protection XIV
- Page Range / eLocation ID:
- 207-226
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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