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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WIP: Interrupt Attack on TEE-protected Robotic Vehicles

Mulong Luo (Cornell University) and G. Edward Suh (Cornell University)

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PHOENIX: Device-Centric Cellular Network Protocol Monitoring using Runtime Verification

Mitziu Echeverria (The University of Iowa), Zeeshan Ahmed (The University of Iowa), Bincheng Wang (The University of Iowa), M. Fareed Arif (The University of Iowa), Syed Rafiul Hussain (Pennsylvania State University), Omar Chowdhury (The University of Iowa)

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Impact Evaluation of Falsified Data Attacks on Connected Vehicle...

Shihong Huang (University of Michigan, Ann Arbor), Yiheng Feng (Purdue University), Wai Wong (University of Michigan, Ann Arbor), Qi Alfred Chen (UC Irvine), Z. Morley Mao and Henry X. Liu (University of Michigan, Ann Arbor) Best Paper Award Runner-up ($200 cash prize)!

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FlowLens: Enabling Efficient Flow Classification for ML-based Network Security...

Diogo Barradas (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Nuno Santos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Luis Rodrigues (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Salvatore Signorello (LASIGE, Faculdade de Ciências, Universidade de Lisboa), Fernando M. V. Ramos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), André Madeira (INESC-ID, Instituto Superior Técnico, Universidade de…

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