Alireza Mohammadi (University of Michigan-Dearborn) and Hafiz Malik (University of Michigan-Dearborn)

Motivated by ample evidence in the automotive cybersecurity literature that the car brake ECUs can be maliciously reprogrammed, it has been shown that an adversary who can directly control the frictional brake actuators can induce wheel lockup conditions despite having a limited knowledge of the tire-road interaction characteristics. In this paper, we investigate the destabilizing effect of such wheel lockup attacks on the lateral motion stability of vehicles from a robust stability perspective. Furthermore, we propose a quadratic programming (QP) problem that the adversary can solve for finding the optimal destabilizing longitudinal slip reference values.

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Model-Agnostic Defense for Lane Detection against Adversarial Attack

Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

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Log4shell: Redefining the Web Attack Surface

Douglas Everson (Clemson University), Long Cheng (Clemson University), and Zhenkai Zhang (Clemson University)

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Tetrad: Actively Secure 4PC for Secure Training and Inference

Nishat Koti (IISc Bangalore), Arpita Patra (IISc Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (IISc, Bangalore)

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MUVIDS: False MAVLink Injection Attack Detection in Communication for...

Seonghoon Jeong, Eunji Park, Kang Uk Seo, Jeong Do Yoo, and Huy Kang Kim (Korea University)

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