Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

The traditional vehicular roll-jam attack is an effective means to gain access to the target vehicle by jamming and recording key fob inputs from a victim. However, it requires specific knowledge of the attack surface, and delicate tuning of software-defined radio parameters. We have developed an enhanced version of the roll-jam attack that uses a known noise signal for jamming, in contrast to the additive white Gaussian noise that is typically used in the attack. Using a known noise signal allows for less strict tuning of the software-defined radios used in the attack, and allows for digital noise removal of the recorded input to enhance the replay attack.

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WIP: A First Look At Employing Large Multimodal Models...

Mohammed Aldeen, Pedram MohajerAnsari, Jin Ma, Mashrur Chowdhury, Long Cheng, Mert D. Pesé (Clemson University)

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Double and Nothing: Understanding and Detecting Cryptocurrency Giveaway Scams

Xigao Li (Stony Brook University), Anurag Yepuri (Stony Brook University), Nick Nikiforakis (Stony Brook University)

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Detection and Resolution of Control Decision Anomalies

Prof. Kang Shin (Kevin and Nancy O'Connor Professor of Computer Science, and the Founding Director of the Real-Time Computing Laboratory (RTCL) in the Electrical Engineering and Computer Science Department at the University of Michigan)

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Smarter Contracts: Detecting Vulnerabilities in Smart Contracts with Deep...

Christoph Sendner (University of Wuerzburg), Huili Chen (University of California San Diego), Hossein Fereidooni (Technische Universität Darmstadt), Lukas Petzi (University of Wuerzburg), Jan König (University of Wuerzburg), Jasper Stang (University of Wuerzburg), Alexandra Dmitrienko (University of Wuerzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt), Farinaz Koushanfar (University of California San Diego)

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