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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C^2SR: Cybercrime Scene Reconstruction for Post-mortem Forensic Analysis

Yonghwi Kwon (University of Virginia), Weihang Wang (University at Buffalo, SUNY), Jinho Jung (Georgia Institute of Technology), Kyu Hyung Lee (University of Georgia), Roberto Perdisci (Georgia Institute of Technology and University of Georgia)

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Demo #10: Security of Deep Learning based Automated Lane...

Takami Sato, Junjie Shen, Ningfei Wang (UC Irvine), Yunhan Jia (ByteDance), Xue Lin (Northeastern University), and Qi Alfred Chen (UC Irvine)

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PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home...

Haotian Chi (Temple University), Qiang Zeng (University of South Carolina), Xiaojiang Du (Temple University), Lannan Luo (University of South Carolina)

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