Test Data Generation for False Data Injection Attack Testing in Air Traffic Surveillance

The ADS-B - Automatic Dependent Surveillance Broadcast - technology requires aircraft to broadcast their position and velocity periodically. The protocol was not specified with cyber security in minds and therefore provides no encryption nor identification. These issues, coupled with the reliance on aircraft to communicate on their status, expose air transport to new cyber security threats, and especially to FDIAs - False Data Injection Attacks - where an attacker modifies, blocks, or emits fake ADS-B messages to dupe controllers and surveillance systems. This paper is part of an ongoing research initiative toward FDIA test generation intended to improve the detection capabilities of surveillance systems. It focuses on the mechanisms used to alter existing legitimate ADS-B recordings as if an attacker had tempered with the communication flow. We propose a set of alteration algorithms covering the taxonomy of FDIA attacks for ADS-B previously defined in the literature. We experiment this approach by generating test data for an AI-based FDIA detection system [8]. Experimental results show that the proposed approach is straightforward to generate attack situations and provides a efficient way to easily generate sophisticated alterations that were not picked up by the detection system.