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 mind 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 the generation of FDIA test scenarios and focuses on the test generation activity, i.e. providing the mechanisms to alter existing ADS-B recordings as if an attacker had tempered with the communication flow, in order to improve the detection capabilities of surveillance systems. 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 [9]. Experimental results show that the proposed approach is straightforward to generate the initial situations used to validate the detection system. Moreover, it provides a efficient way to easily generate sophisticated alterations that were not picked up by the detection system.
Major transportation surveillance protocols have not been specified with cyber security in mind and therefore provide no encryption nor identification. These issues expose air and sea transport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fake surveillance messages to dupe controllers and surveillance systems. There has been growing interest in conducting research on machine learning-based anomaly detection systems that address these new threats. However, significant amounts of data are needed to achieve meaningful results with this type of model. Raw, genuine data can be obtained from existing databases but need to be preprocessed before being fed to a model. Acquiring anomalous data is another challenge: such data is much too scarce for both the Automatic Dependent Surveillance–Broadcast (ADS-B) and the Automatic Identification System (AIS). Crafting anomalous data by hand, which has been the sole method applied to date, is hardly suitable for broad detection model testing. This paper proposes an approach built upon existing libraries and ideas that offers ML researchers the necessary tools to facilitate the access and processing of genuine data as well as to automatically generate synthetic anomalous surveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of the approach by discussing work in progress that includes the reproduction of related work, creation of relevant datasets and design of advanced anomaly detection models for both domains of application.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.