Paul Agbaje, Abraham Mookhoek, Afia Anjum, Arkajyoti Mitra (University of Texas at Arlington), Mert D. Pesé (Clemson University), Habeeb Olufowobi (University of Texas at Arlington)

Millions of lives are lost due to road accidents each year, emphasizing the importance of improving driver safety measures. In addition, physical vehicle security is a persistent challenge exacerbated by the growing interconnectivity of vehicles, allowing adversaries to engage in vehicle theft and compromising driver privacy. The integration of advanced sensors with internet connectivity has ushered in the era of intelligent transportation systems (ITS), enabling vehicles to generate abundant data that facilitates diverse vehicular applications. These data can also provide insights into driver behavior, enabling effective driver monitoring to support safety and security. In this paper, we propose AutoWatch, a graph-based approach for modeling the behavior of drivers, verifying the identity of the driver, and detecting unsafe driving maneuvers. Our evaluation shows that AutoWatch can improve driver identification accuracy by up to 22% and driving maneuver classification by up to 5.7% compared to baseline techniques.

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Adryana Hutchinson (The George Washington University), Jinwei Tang (Clark University), Adam Aviv (The George Washington University), Peter Story (Clark University)

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Ryunosuke Kobayashi, Kazuki Nomoto, Yuna Tanaka, Go Tsuruoka (Waseda University), Tatsuya Mori (Waseda University/NICT/RIKEN)

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EMMasker: EM Obfuscation Against Website Fingerprinting

Mohammed Aldeen, Sisheng Liang, Zhenkai Zhang, Linke Guo (Clemson University), Zheng Song (University of Michigan – Dearborn), and Long Cheng (Clemson University)

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CrowdGuard: Federated Backdoor Detection in Federated Learning

Phillip Rieger (Technical University of Darmstadt), Torsten Krauß (University of Würzburg), Markus Miettinen (Technical University of Darmstadt), Alexandra Dmitrienko (University of Würzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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