Shiqing Luo (George Mason University), Anh Nguyen (George Mason University), Hafsa Farooq (Georgia State University), Kun Sun (George Mason University), Zhisheng Yan (George Mason University)

Understanding the vulnerability of virtual reality (VR) is crucial for protecting sensitive data and building user trust in VR ecosystems. Previous attacks have demonstrated the feasibility of inferring VR keystrokes inside head-mounted displays (HMDs) by recording side-channel signals generated during user-HMD interactions. However, these attacks are heavily constrained by the physical layout or victim pose in the attack scenario since the recording device must be strictly positioned and oriented in a particular way with respect to the victim. In this paper, we unveil a placement-flexible keystroke inference attack in VR by eavesdropping the clicking sounds of the moving hand controller during keystrokes. The malicious recording smartphone can be placed anywhere surrounding the victim, making the attack more flexible and practical to deploy in VR environments. As the first acoustic attack in VR, our system, Heimdall, overcomes unique challenges unaddressed by previous acoustic attacks on physical keyboards and touchscreens. These challenges include differentiating sounds in a 3D space, adaptive mapping between keystroke sound and key in varying recording placement, and handling occasional hand rotations. Experiments with 30 participants show that Heimdall achieves key inference accuracy of 96.51% and top-5 accuracy of 85.14%-91.22% for inferring passwords with 4-8 characters. Heimdall is also robust under various practical impacts such as smartphone-user placement, attack environments, hardware models, and victim conditions.

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Hao-Ping (Hank) Lee (Carnegie Mellon University), Wei-Lun Kao (National Taiwan University), Hung-Jui Wang (National Taiwan University), Ruei-Che Chang (University of Michigan), Yi-Hao Peng (Carnegie Mellon University), Fu-Yin Cherng (National Chung Cheng University), Shang-Tse Chen (National Taiwan University)

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Towards Real-time Voice Interaction Data Collection Monitoring and Ambient...

Tu Le (University of California, Irvine), Zixin Wang (Zhejiang University), Danny Yuxing Huang (New York University), Yaxing Yao (Virginia Tech), Yuan Tian (University of California, Los Angeles)

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LoRDMA: A New Low-Rate DoS Attack in RDMA Networks

Shicheng Wang (Tsinghua University), Menghao Zhang (Beihang University & Infrawaves), Yuying Du (Information Engineering University), Ziteng Chen (Southeast University), Zhiliang Wang (Tsinghua University & Zhongguancun Laboratory), Mingwei Xu (Tsinghua University & Zhongguancun Laboratory), Renjie Xie (Tsinghua University), Jiahai Yang (Tsinghua University & Zhongguancun Laboratory)

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