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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Bernoulli Honeywords

Ke Coby Wang (Duke University), Michael K. Reiter (Duke University)

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IDA: Hybrid Attestation with Support for Interrupts and TOCTOU

Fatemeh Arkannezhad (UCLA), Justin Feng (UCLA), Nader Sehatbakhsh (UCLA)

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OCPPStorm: A Comprehensive Fuzzing Tool for OCPP Implementations (Long)

Gaetano Coppoletta (University of Illinois Chicago), Rigel Gjomemo (Discovery Partners Institute, University of Illinois), Amanjot Kaur, Nima Valizadeh (Cardiff University), Venkat Venkatakrishnan (Discovery Partners Institute, University of Illinois), Omer Rana (Cardiff University)

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HEIR: A Unified Representation for Cross-Scheme Compilation of Fully...

Song Bian (Beihang University), Zian Zhao (Beihang University), Zhou Zhang (Beihang University), Ran Mao (Beihang University), Kohei Suenaga (Kyoto University), Yier Jin (University of Science and Technology of China), Zhenyu Guan (Beihang University), Jianwei Liu (Beihang University)

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