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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TALISMAN: Tamper Analysis for Reference Monitors

Frank Capobianco (The Pennsylvania State University), Quan Zhou (The Pennsylvania State University), Aditya Basu (The Pennsylvania State University), Trent Jaeger (The Pennsylvania State University, University of California, Riverside), Danfeng Zhang (The Pennsylvania State University, Duke University)

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A Comparison of Three Approaches to Assist Users in...

Michael Clark (Brigham Young University), Scott Ruoti (The University of Tennessee), Michael Mendoza (Imperial College London), Kent Seamons (Brigham Young University)

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Analyzing and Creating Malicious URLs: A Comparative Study on...

Vincent Drury (IT-Security Research Group, RWTH Aachen University), Rene Roepke (Learning Technologies Research Group, RWTH Aachen University), Ulrik Schroeder (Learning Technologies Research Group, RWTH Aachen University), Ulrike Meyer (IT-Security Research Group, RWTH Aachen University)

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TEE-SHirT: Scalable Leakage-Free Cache Hierarchies for TEEs

Kerem Arikan (Binghamton University), Abraham Farrell (Binghamton University), Williams Zhang Cen (Binghamton University), Jack McMahon (Binghamton University), Barry Williams (Binghamton University), Yu David Liu (Binghamton University), Nael Abu-Ghazaleh (University of California, Riverside), Dmitry Ponomarev (Binghamton University)

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