Huadi Zhu (The University of Texas at Arlington), Mingyan Xiao (The University of Texas at Arlington), Demoria Sherman (The University of Texas at Arlington), Ming Li (The University of Texas at Arlington)

Virtual Reality (VR) has shown promising potential in many applications, such as e-business, healthcare, and social networking. Rich information regarding users' activities and online accounts is stored in VR devices. If {they are} carelessly unattended, adversarial access will cause data breaches and other critical consequences. Practical user authentication schemes for VR devices are in dire need. Current solutions, including passwords, digital PINs, and pattern locks, mostly follow conventional approaches for general personal devices. They have been criticized for deficits in both security and usability. In this work, we propose SoundLock, a novel user authentication scheme for VR devices using auditory-pupillary response as biometrics. During authentication, auditory stimuli are presented to the user via the VR headset. The corresponding pupillary response is captured by the integrated eye tracker. User's legitimacy is then determined by comparing the response with the template generated during the enrollment stage. To strike a balance between security and usability in the scheme design, an optimization problem is formulated. Due to its non-linearity, a two-stage heuristic algorithm is proposed to solve it efficiently. The solution provides necessary guidance for selecting effective auditory stimuli and determining their corresponding lengths. We demonstrate through extensive in-field experiments that SoundLock outperforms state-of-the-art biometric solutions with FAR (FRR) as low as 0.76% (0.91%) and is well received among participants in the user study.

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Kai Wang (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Wenqi Chen (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University), Xia Yin (Tsinghua University)

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DOITRUST: Dissecting On-chain Compromised Internet Domains via Graph Learning

Shuo Wang (CSIRO's Data61 & Cybersecurity CRC, Australia), Mahathir Almashor (CSIRO's Data61 & Cybersecurity CRC, Australia), Alsharif Abuadbba (CSIRO's Data61 & Cybersecurity CRC, Australia), Ruoxi Sun (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Calvin Wang (CSIRO's Data61), Raj Gaire (CSIRO's Data61 & Cybersecurity CRC, Australia), Surya Nepal (CSIRO's Data61 & Cybersecurity CRC, Australia), Seyit Camtepe (CSIRO's…

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Semi-Automated Synthesis of Driving Rules

Diego Ortiz, Leilani Gilpin, Alvaro A. Cardenas (University of California, Santa Cruz)

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