Detection of cyber attacks targeting avionics systems

Abstract

The present subject matter provides various technical solutions to technical problems facing ADS-B cyber-attacks. One technical solution for detecting and mitigating ADS-B cyber-attacks includes receiving extracting information from received ADS-B signals, detecting a cyber-attack based on a selected subset of ADS-B information, determining a detection probability, and outputting a ADS-B cyber-attack type and probability. This solution may further include determining and implementing a cyber-attack mitigation to reduce the probability or effect of the detected cyber-attack. These solutions operate based on current ADS-B receiver technology, and can be combined with existing ADS-B receivers to detect message injection attacks, modification attacks, and jamming attacks. The technical solutions described herein use machine learning (ML) algorithms and statistical models to detect anomalies in incoming ADS-B messages. This enables these solutions to be trained in different environments, which further improves the cyber-attack detection accuracy and reduces likelihood of false alarms or miss detections.