Anas Alsoliman, Marco Levorato, and Qi Alfred Chen (UC Irvine)

In autonomous vehicle systems – whether ground or aerial – vehicles and infrastructure-level units communicate among each other continually to ensure safe and efficient autonomous operations. However, different attack scenarios might arise in such environments when a device in the network cannot physically pinpoint the actual transmitter of a certain message. For example, a compromised or a malicious vehicle could send a message with a fabricated location to appear as if it is in the location of another legitimate vehicle, or fabricate multiple messages with fake identities to alter the behavior of other vehicles/infrastructure units and cause traffic congestion or accidents. In this paper, we propose a Vision-Based Two-Factor Authentication and Localization Scheme for Autonomous Vehicles. The scheme leverages the vehicles’ light sources and cameras to establish an “Optical Camera Communication (OCC)” channel providing an auxiliary channel between vehicles to visually authenticate and localize the transmitter of messages that are sent over Radio Frequency (RF) channels. Additionally, we identify possible attacks against the proposed scheme as well as mitigation strategies.

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Dinosaur Resurrection: PowerPC Binary Patching for Base Station Analysis

Uwe Muller, Eicke Hauck, Timm Welz, Jiska Classen, Matthias Hollick (Secure Mobile Networking Lab, TU Darmstadt)

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CHANCEL: Efficient Multi-client Isolation Under Adversarial Programs

Adil Ahmad (Purdue University), Juhee Kim (Seoul National University), Jaebaek Seo (Google), Insik Shin (KAIST), Pedro Fonseca (Purdue University), Byoungyoung Lee (Seoul National University)

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Demo #11: Understanding the Effects of Paint Colors on...

Shaik Sabiha (University at Buffalo), Keyan Guo (University at Buffalo), Foad Hajiaghajani (University at Buffalo), Chunming Qiao (University at Buffalo), Hongxin Hu (University at Buffalo) and Ziming Zhao (University at Buffalo)

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