Tao Ni (City University of Hong Kong), Yuefeng Du (City University of Hong Kong), Qingchuan Zhao (City University of Hong Kong), Cong Wang (City University of Hong Kong)

Virtual Reality (VR) technologies are increasingly employed in numerous applications across various areas. Therefore, it is essential to ensure the security of interactions between users and VR devices. In this paper, we disclose a new side-channel leakage in the constellation tracking system of mainstream VR platforms, where the infrared (IR) signals emitted from the VR controllers for controller-headset interactions can be maliciously exploited to reconstruct unconstrained input keystrokes on the virtual keyboard non-intrusively. We propose a novel keystroke inference attack named VRecKey to demonstrate the feasibility and practicality of this novel infrared side channel. Specifically, VRecKey leverages a customized 2D IR sensor array to intercept ambient IR signals emitted from VR controllers and subsequently infers (i) character-level key presses on the virtual keyboard and (ii) word-level keystrokes along with their typing trajectories. We extensively evaluate the effectiveness of VRecKey with two commercial VR devices, and the results indicate that it can achieve over 94.2% and 90.5% top-3 accuracy in inferring character-level and word-level keystrokes with varying lengths, respectively. In addition, empirical results show that VRecKey is resilient to several practical impact factors and presents effectiveness in various real-world scenarios, which provides a complementary and orthogonal attack surface for the exploration of keystroke inference attacks in VR platforms.

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Distributed Function Secret Sharing and Applications

Pengzhi Xing (University of Electronic Science and Technology of China), Hongwei Li (University of Electronic Science and Technology of China), Meng Hao (Singapore Management University), Hanxiao Chen (University of Electronic Science and Technology of China), Jia Hu (University of Electronic Science and Technology of China), Dongxiao Liu (University of Electronic Science and Technology of China)

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Evaluating Machine Learning-Based IoT Device Identification Models for Security...

Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

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Oreo: Protecting ASLR Against Microarchitectural Attacks

Shixin Song (Massachusetts Institute of Technology), Joseph Zhang (Massachusetts Institute of Technology), Mengjia Yan (Massachusetts Institute of Technology)

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