Michele Marazzi, Stefano Longari, Michele Carminati, Stefano Zanero (Politecnico di Milano)

ZOOX AutoDriving Security Award Runner-up!

With the increasing interest in autonomous vehicles (AVs), ensuring their safety and security is becoming crucial. The introduction of advanced features has increased the need for various interfaces to communicate with the external world, creating new potential attack vectors that attackers can exploit to alter sensor data. LiDAR sensors are widely employed to support autonomous driving features and generate point cloud data used by ADAS to 3D map the vehicle’s surroundings. Tampering attacks on LiDAR-generated data can compromise the vehicle’s functionalities and seriously threaten passengers and other road users. Existing approaches to LiDAR data tampering detection show security flaws and can be bypassed by attackers through design vulnerabilities. This paper proposes a novel approach for tampering detection of LiDAR-generated data in AVs, employing a watermarking technique. We validate our approach through experiments to prove its feasibility in realworld time-constrained scenarios and its efficacy in detecting LiDAR tampering attacks. Our approach performs better when compared to the current state-of-the-art LiDAR watermarking techniques while addressing critical issues related to watermark security and imperceptibility.

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Facilitating Non-Intrusive In-Vivo Firmware Testing with Stateless Instrumentation

Jiameng Shi (University of Georgia), Wenqiang Li (Independent Researcher), Wenwen Wang (University of Georgia), Le Guan (University of Georgia)

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CBAT: A Comparative Binary Analysis Tool

Chloe Fortuna (STR), JT Paasch (STR), Sam Lasser (Draper), Philip Zucker (Draper), Chris Casinghino (Jane Street), Cody Roux (AWS)

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the...

Man Zhou (Huazhong University of Science and Technology), Shuao Su (Huazhong University of Science and Technology), Qian Wang (Wuhan University), Qi Li (Tsinghua University), Yuting Zhou (Huazhong University of Science and Technology), Xiaojing Ma (Huazhong University of Science and Technology), Zhengxiong Li (University of Colorado Denver)

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Unus pro omnibus: Multi-Client Searchable Encryption via Access Control

Jiafan Wang (Data61, CSIRO), Sherman S. M. Chow (The Chinese University of Hong Kong)

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