ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection

Area of Science:

• Aerospace Engineering
• Cybersecurity
• Signal Processing

Background:

• Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical air traffic control system.
• ADS-B offers advantages over traditional radar but has vulnerabilities due to its reliance on GNSS and unencrypted protocols.
• These vulnerabilities expose the system to attacks like jamming, spoofing, and false message injection.

Purpose of the Study:

• To develop and validate a robust method for detecting various attacks targeting the ADS-B system.
• To enhance the security and reliability of air traffic surveillance.
• To address the limitations of the current ADS-B protocol.

Main Methods:

• Mathematical modeling of different attack scenarios.
• Implementation of a crowd sensor network for Time Difference Of Arrival (TDOA) estimation of ADS-B messages.
• Application of a two-step Kalman filter for attack detection.

Main Results:

• The proposed algorithm effectively detects on-board GNSS/ADS-B tampering.
• Successful detection of false ADS-B message injection and GNSS spoofing/jamming attacks.
• Achieved a very high probability of detection with a low probability of false alarms in tests and simulations.

Conclusions:

• The developed crowd-sensing and Kalman filtering approach provides a highly effective solution for ADS-B security.
• This method significantly improves the resilience of air traffic surveillance against cyber threats.
• The findings contribute to the advancement of secure and reliable aviation communication systems.