Takami Sato, Ningfei Wang (University of California, Irvine), Yueqiang Cheng (NIO Security Research), Qi Alfred Chen (University of California, Irvine)

Automated Lane Centering (ALC) is one of the most popular autonomous driving (AD) technologies available in many commodity vehicles. ALC can reduce the human driver’s efforts by taking over their steering work. However, recent research alerts that ALC can be vulnerable to off-road attacks that lead victim vehicles out of their driving lane. To be secure against off-road attacks, this paper explores the potential defense capability of low-quality localization and publicly available maps against off-road attacks against autonomous driving. We design the first map-fusion-based off-road attack detection approach, LaneGuard, LaneGuard detects off-road attacks based on the difference between the observed road shape and the driver-predefined route shape. We evaluate LaneGuar on large-scale real-world driving traces consisting of 80 attack scenarios and 11,558 benign scenarios. We find that LaneGuard can achieve an attack detection rate of 89% with a 12% false positive rate. In real-world highway driving experiments, LaneGuard exhibits no false positives while maintaining a near-zero false negative rate against simulated attacks.

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Abusing the Ethereum Smart Contract Verification Services for Fun...

Pengxiang Ma (Huazhong University of Science and Technology), Ningyu He (Peking University), Yuhua Huang (Huazhong University of Science and Technology), Haoyu Wang (Huazhong University of Science and Technology), Xiapu Luo (The Hong Kong Polytechnic 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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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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Aligning Confidential Computing with Cloud-native ML Platforms

Angelo Ruocco, Chris Porter, Claudio Carvalho, Daniele Buono, Derren Dunn, Hubertus Franke, James Bottomley, Marcio Silva, Mengmei Ye, Niteesh Dubey, Tobin Feldman-Fitzthum (IBM Research)

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