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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Dev Vikesh Doshi (California State University San Marcos), Mehjabeen Tasnim (California State University San Marcos), Fernando Landeros (California State University San Marcos), Chinthagumpala Muni Venkatesh (California State University San Marcos), Daniel Timko (Emerging Threats Lab / Smishtank.com), Muhammad Lutfor Rahman (California State University San Marcos)

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Rishika Thorat (Purdue University), Tatiana Ringenberg (Purdue University)

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Timing Channels in Adaptive Neural Networks

Ayomide Akinsanya (Stevens Institute of Technology), Tegan Brennan (Stevens Institute of Technology)

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Experimental Analyses of the Physical Surveillance Risks in Client-Side...

Ashish Hooda (University of Wisconsin-Madison), Andrey Labunets (UC San Diego), Tadayoshi Kohno (University of Washington), Earlence Fernandes (UC San Diego)

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