Tobias Lüscher (ETH Zurich), Martin Strohmeier (Cyber-Defence Campus, armasuisse S+T), Vincent Lenders (Cyber-Defence Campus, armasuisse S+T)

Automatic Dependent Surveillance - Contract (ADS-C) is an satellite-based aviation datalink application used to monitor aircraft in remote regions. It is a crucial method for air traffic control to track aircraft where other protocols such as ADS-B lack connectivity. Even though it has been conceived more than 30 years ago, and other legacy communication protocols in aviation have shown to be vulnerable, ADS-C’s security has not been investigated so far in the literature. We conduct a first investigation to close this gap. First, we compile a comprehensive overview of the history, impact, and technical details of ADSC and its lower layers. Second, we build two software-defined radio receivers in order to analyze over 120’000 real-world ADSC messages. We further illustrate ADS-C’s lack of authentication by implementing an ADS-C transmitter, which is capable of generating and sending arbitrary ADS-C messages. Finally, we use the channel control offered through a software-defined ADSC receiver and transmitter as a basis for an in-depth analysis of the protocol weaknesses of the ADS-C system. The found vulnerabilities range from passively tracking aircraft to actively altering the position of actual aircraft through attacks on the downlink and the uplink. We assess the difficulty and impact of these attacks and discuss potential countermeasures.

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Benchmarking transferable adversarial attacks

Zhibo Jin (The University of Sydney), Jiayu Zhang (Suzhou Yierqi), Zhiyu Zhu, Huaming Chen (The University of Sydney)

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Facilitating Threat Modeling by Leveraging Large Language Models

Isra Elsharef, Zhen Zeng (University of Wisconsin-Milwaukee), Zhongshu Gu (IBM Research)

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The CURE to Vulnerabilities in RPKI Validation

Donika Mirdita (Technische Universität Darmstadt), Haya Schulmann (Goethe-Universität Frankfurt), Niklas Vogel (Goethe-Universität Frankfurt), Michael Waidner (Technische Universität Darmstadt, Fraunhofer SIT)

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ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Linkang Du (Zhejiang University), Min Chen (CISPA Helmholtz Center for Information Security), Mingyang Sun (Zhejiang University), Shouling Ji (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University), Zhikun Zhang (CISPA Helmholtz Center for Information Security and Stanford University)

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