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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Using Behavior Monitoring to Identify Privacy Concerns in Smarthome...

Atheer Almogbil, Momo Steele, Sofia Belikovetsky (Johns Hopkins University), Adil Inam (University of Illinois at Urbana-Champaign), Olivia Wu (Johns Hopkins University), Aviel Rubin (Johns Hopkins University), Adam Bates (University of Illinois at Urbana-Champaign)

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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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5G-Spector: An O-RAN Compliant Layer-3 Cellular Attack Detection Service

Haohuang Wen (The Ohio State University), Phillip Porras (SRI International), Vinod Yegneswaran (SRI International), Ashish Gehani (SRI International), Zhiqiang Lin (The Ohio State University)

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WIP: Towards a Certifiably Robust Defense for Multi-label Classifiers...

Dennis Jacob, Chong Xiang, Prateek Mittal (Princeton University)

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