Matthew Smith (University of Oxford), Martin Strohmeier (University of Oxford), Jonathan Harman (Vrije Universiteit Amsterdam), Vincent Lenders (armasuisse Science and Technology), Ivan Martinovic (University of Oxford)

Many wireless communications systems found in aircraft lack standard security mechanisms, leaving them fundamentally vulnerable to attack. With affordable software-defined radios available, a novel threat has emerged, allowing a wide range of attackers to easily interfere with wireless avionic systems. Whilst these vulnerabilities are known, concrete attacks that exploit them are still novel and not yet well understood. This is true in particular with regards to their kinetic impact on the handling of the attacked aircraft and consequently its safety. To investigate this, we invited 30 Airbus A320 type-rated pilots to fly simulator scenarios in which they were subjected to attacks on their avionics. We implement and analyze novel wireless attacks on three safety-related systems: Traffic Collision Avoidance System (TCAS), Ground Proximity Warning System (GPWS) and the Instrument Landing System (ILS). We found that all three analyzed attack scenarios created significant control impact and cost of disruption through turnarounds, avoidance manoeuvres, and diversions. They further increased workload, distrust in the affected system, and in 38% of cases caused the attacked safety system to be switched off entirely. All pilots felt the scenarios were useful, with 93.3% feeling that specific simulator training for wireless attacks could be valuable.

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ConTExT: A Generic Approach for Mitigating Spectre

Michael Schwarz (Graz University of Technology), Moritz Lipp (Graz University of Technology), Claudio Canella (Graz University of Technology), Robert Schilling (Graz University of Technology and Know-Center GmbH), Florian Kargl (Graz University of Technology), Daniel Gruss (Graz University of Technology)

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SVLAN: Secure & Scalable Network Virtualization

Jonghoon Kwon (ETH), Taeho Lee (ETH), Claude Hähni (ETH), Adrian Perrig (ETH)

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Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Harsh Chaudhari (Indian Institute of Science, Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (Indian Institute of Science, Bangalore)

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Into the Deep Web: Understanding E-commerce Fraud from Autonomous...

Peng Wang (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington), Yue Qin (Indiana University Bloomington), XiaoFeng Wang (Indiana University Bloomington)

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