Publication | Closed Access
Investigation of multi-device location spoofing attacks on air traffic control and possible countermeasures
53
Citations
33
References
2016
Year
Unknown Venue
EngineeringLocation EstimationInformation SecuritySide-channel AttackLocalizationMisbehaviour DetectionWireless SecurityMulti-device LocationSecure CommunicationAircraft LocalizationAir Traffic ControlComputer ScienceRf LocalizationSignal ProcessingData SecurityPossible CountermeasuresMultilateration TechniquesWireless Localization SystemsControl System SecurityCountermeasure
Multilateration techniques have been proposed to verify the integrity of unprotected location claims in wireless localization systems. A common assumption is that the adversary is equipped with only a single device from which it transmits location spoofing signals. In this paper, we consider a more advanced model where the attacker is equipped with multiple devices and performs a geographically distributed coordinated attack on the multilateration system. The feasibility of a distributed multi-device attack is demonstrated experimentally with a self-developed attack implementation based on multiple COTS software-defined radio (SDR) devices. We launch an attack against the OpenSky Network, an air traffic surveillance system that implements a time-difference-of-arrival (TDoA) multi-lateration method for aircraft localization based on ADS-B signals. Our experiments show that the timing errors for distributed spoofed signals are indistinguishable from the multilateration errors of legitimate aircraft signals, indicating that the threat of multi-device spoofing attacks is real in this and other similar systems. In the second part of this work, we investigate physical-layer features that could be used to detect multi-device attacks. We show that the frequency offset and transient phase noise of the attacker's radio devices can be exploited to discriminate between a received signal that has been transmitted by a single (legitimate) transponder or by multiple (malicious) spoofing sources. Based on that, we devise a multi-device spoofing detection system that achieves zero false positives and a false negative rate below 1%.
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