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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LibAFL QEMU: A Library for Fuzzing-oriented Emulation

Romain Malmain (EURECOM), Andrea Fioraldi (EURECOM), Aurelien Francillon (EURECOM)

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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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AutoWatch: Learning Driver Behavior with Graphs for Auto Theft...

Paul Agbaje, Abraham Mookhoek, Afia Anjum, Arkajyoti Mitra (University of Texas at Arlington), Mert D. Pesé (Clemson University), Habeeb Olufowobi (University of Texas at Arlington)

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CAN-MIRGU: A Comprehensive CAN Bus Attack Dataset from Moving...

Sampath Rajapaksha, Harsha Kalutarage (Robert Gordon University, UK), Garikayi Madzudzo (Horiba Mira Ltd, UK), Andrei Petrovski (Robert Gordon University, UK), M.Omar Al-Kadri (University of Doha for Science and Technology)

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