Noah T. Curran (University of Michigan), Kang G. Shin (University of Michigan), William Hass (Lear Corporation), Lars Wolleschensky (Lear Corporation), Rekha Singoria (Lear Corporation), Isaac Snellgrove (Lear Corporation), Ran Tao (Lear Corporation)

ETAS Best Short Paper Award Runner-Up!

On urban roadways, “dooring” remains a serious problem to the safety of pedestrians, cyclists, and other vulnerable road users (VRUs). Existing solutions that address this concern remain inadequate, as they either place unreasonable expectations on the pedestrians or rely on prohibitively expensive additions to the vehicle’s sensing capabilities. Consequently, typical consumer vehicles are not yet equipped with such a technology, and practical dooring prevention still remains a safety concern.
To address this problem, we propose a driver safety system for dooring prevention called S-Door that uses existing resources available in every modern vehicle: Bluetooth Low-Energy (BLE). Since a modern vehicle is distributively equipped with multiple BLE transceivers, we leverage each transceiver to observe BLE advertising data (AD) packets that consumers’ smart devices passively transmit. From these AD packets, we extract information that we can use to localize the VRU device without pairing with the device. With this information, we propose two methods for localization based on BLE versions ≤5.0 and ≥5.1, respectively. Our solutions are capable of alerting the driver of all instances of an oncoming VRU. Due to S-Door’s use of existing vehicle BLE hardware, we may extend this application to modern vehicles through a firmware update—no physical modification is necessary.

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Focusing on Pinocchio's Nose: A Gradients Scrutinizer to Thwart...

Jiayun Fu (Huazhong University of Science and Technology), Xiaojing Ma (Huazhong University of Science and Technology), Bin B. Zhu (Microsoft Research Asia), Pingyi Hu (Huazhong University of Science and Technology), Ruixin Zhao (Huazhong University of Science and Technology), Yaru Jia (Huazhong University of Science and Technology), Peng Xu (Huazhong University of Science and Technology), Hai…

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Preventing SIM Box Fraud Using Device Model Fingerprinting

BeomSeok Oh (KAIST), Junho Ahn (KAIST), Sangwook Bae (KAIST), Mincheol Son (KAIST), Yonghwa Lee (KAIST), Min Suk Kang (KAIST), Yongdae Kim (KAIST)

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Cyber Threat Intelligence for SOC Analysts

Nidhi Rastogi, Md Tanvirul Alam (Rochester Institute of Technology)

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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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