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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KUBO: Precise and Scalable Detection of User-triggerable Undefined Behavior...

Changming Liu (Northeastern University), Yaohui Chen (Facebook Inc.), Long Lu (Northeastern University)

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Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing

Jinghan Wang (University of California, Riverside), Chengyu Song (University of California, Riverside), Heng Yin (University of California, Riverside)

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Does Every Second Count? Time-based Evolution of Malware Behavior...

Alexander Küchler (Fraunhofer AISEC), Alessandro Mantovani (EURECOM), Yufei Han (NortonLifeLock Research Group), Leyla Bilge (NortonLifeLock Research Group), Davide Balzarotti (EURECOM)

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