Imani N. S. Munyaka (University of California, San Diego), Daniel A Delgado, Juan Gilbert, Jaime Ruiz, Patrick Traynor (University of Florida)

Telephone carriers and third-party developers have created technical solutions to detect and notify consumers of spam calls. The goal of this technology is to help users make decisions about incoming calls and reduce the negative effects of spam calls on finances and daily life. Although useful, this technology has varying accuracy due to technical limitations. In this study, we conduct design interviews, a call response diary study, and an MTurk survey (N=143) to explore the relationship between warning accuracy and callee decision-making for incoming calls. Our results suggest that previous call experience can lead to incomplete mental models of how Caller ID works. Additionally, we find that false alarms and missed detection do not impact call response but can influence user expectations of the call. Since adversaries can use mismatched expectations to their advantage, we recommend using warning design characteristics that align with user expectations under detection accuracy constraints.

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Xigao Li (Stony Brook University), Amir Rahmati (Stony Brook University), Nick Nikiforakis (Stony Brook University)

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Predictive Context-sensitive Fuzzing

Pietro Borrello (Sapienza University of Rome), Andrea Fioraldi (EURECOM), Daniele Cono D'Elia (Sapienza University of Rome), Davide Balzarotti (Eurecom), Leonardo Querzoni (Sapienza University of Rome), Cristiano Giuffrida (Vrije Universiteit Amsterdam)

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Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech...

Xinfeng Li (Zhejiang University), Chen Yan (Zhejiang University), Xuancun Lu (Zhejiang University), Zihan Zeng (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

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