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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Detecting Voice Cloning Attacks via Timbre Watermarking

Chang Liu (University of Science and Technology of China), Jie Zhang (Nanyang Technological University), Tianwei Zhang (Nanyang Technological University), Xi Yang (University of Science and Technology of China), Weiming Zhang (University of Science and Technology of China), NengHai Yu (University of Science and Technology of China)

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IDA: Hybrid Attestation with Support for Interrupts and TOCTOU

Fatemeh Arkannezhad (UCLA), Justin Feng (UCLA), Nader Sehatbakhsh (UCLA)

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Beyond the Surface: Uncovering the Unprotected Components of Android...

Hao Zhou (The Hong Kong Polytechnic University), Shuohan Wu (The Hong Kong Polytechnic University), Chenxiong Qian (University of Hong Kong), Xiapu Luo (The Hong Kong Polytechnic University), Haipeng Cai (Washington State University), Chao Zhang (Tsinghua University)

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Improving the Robustness of Transformer-based Large Language Models with...

Lujia Shen (Zhejiang University), Yuwen Pu (Zhejiang University), Shouling Ji (Zhejiang University), Changjiang Li (Penn State), Xuhong Zhang (Zhejiang University), Chunpeng Ge (Shandong University), Ting Wang (Penn State)

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