Ryo Suzuki (Keio University), Takami Sato (University of California, Irvine), Yuki Hayakawa, Kazuma Ikeda, Ozora Sako, Rokuto Nagata (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

LiDAR (Light Detection and Ranging) is an essential sensor for autonomous driving (AD), increasingly being integrated not only in prototype vehicles but also in commodity vehicles. Due to its critical safety implications, recent studies have explored its security risks and exposed the potential vulnerability against LiDAR spoofing attacks, which manipulate measurement data by emitting malicious lasers into the LiDAR. Nevertheless, deploying LiDAR spoofing attacks against driving AD vehicles still has significant technical challenges particularly in accurately aiming at the LiDAR of a moving AV from the roadside. The current state-of-the-art attack can be successful only at ≤5 km/h. Motivated by this, we design novel tracking and aiming methodology and conduct a feasibility study to explore the actual practicality of LiDAR spoofing attacks against AD vehicles at cruising speeds. In this work, we report our initial results demonstrating that our object removal attack successfully makes the targeted pedestrian undetectable with ≥90% success rates in a real-world scenario where the adversary at the roadside attacks the victim AD approaching at 35 km/h. Finally, we discuss the current challenges and our future plans.

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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Enhanced Vehicular Roll-Jam Attack using a Known Noise Source

Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

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Free Proxies Unmasked: A Vulnerability and Longitudinal Analysis of...

Naif Mehanna (Univ. Lille / Inria / CNRS), Walter Rudametkin (IRISA / Univ Rennes), Pierre Laperdrix (CNRS, Univ Lille, Inria Lille), and Antoine Vastel (Datadome)

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EyeSeeIdentity: Exploring Natural Gaze Behaviour for Implicit User Identification...

L Yasmeen Abdrabou (Lancaster University), Mariam Hassib (Fortiss Research Institute of the Free State of Bavaria), Shuqin Hu (LMU Munich), Ken Pfeuffer (Aarhus University), Mohamed Khamis (University of Glasgow), Andreas Bulling (University of Stuttgart), Florian Alt (University of the Bundeswehr Munich)

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