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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Megan Nyre-Yu (Sandia National Laboratories), Elizabeth S. Morris (Sandia National Laboratories), Blake Moss (Sandia National Laboratories), Charles Smutz (Sandia National Laboratories), Michael R. Smith (Sandia National Laboratories)

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Understanding and Analyzing Appraisal Systems in the Underground Marketplaces

Zhengyi Li (Indiana University Bloomington), Xiaojing Liao (Indiana University Bloomington)

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It’s Standards’ Time to Shine: Insights for IoT Cybersecurity...

Dr. Michael J. Fagan, National Institute of Standards and Technology

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GraphGuard: Detecting and Counteracting Training Data Misuse in Graph...

Bang Wu (CSIRO's Data61/Monash University), He Zhang (Monash University), Xiangwen Yang (Monash University), Shuo Wang (CSIRO's Data61/Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Shirui Pan (Griffith University), Xingliang Yuan (Monash University)

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