Fengchen Yang (Zhejiang University), Wenze Cui (Zhejiang University), Xinfeng Li (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

Fluorescent lamps are almost everywhere for electric lighting in daily life, across private and public scenarios. Our study uncovers a new electromagnetic interference (EMI) attack surface that these light sources are actually able to manipulate nearby IoT devices in a contactless way. Different from previous EMI attempts requiring a specialized metal antenna as the emission source, which can easily alert victims, we introduce LightAntenna that leverages unaltered everyday fluorescent lamps to launch concealed EMI attacks. To understand why and how fluorescent lamps can be exploited as malicious antennas, we systematically characterize the rationale of EMI emission from fluorescent lamps and identify their capabilities and limits in terms of intensity and frequency response. Moreover, we carefully design a covert method of injecting high-frequency signals into the fluorescent tube via power line transmission. In this way, LightAntenna can realize controllable EMI attacks even across rooms and at a distance of up to 20 m. Our extensive experiments demonstrate the generality, practicality, tunability, and remote attack capability of LightAntenna, which successfully interferes with various types of sensors and IoT devices. In summary, our study provides a comprehensive analysis of the LightAntenna mechanism and proposes defensive strategies to mitigate this emerging attack surface.

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Aleksei Stafeev (CISPA Helmholtz Center for Information Security), Tim Recktenwald (CISPA Helmholtz Center for Information Security), Gianluca De Stefano (CISPA Helmholtz Center for Information Security), Soheil Khodayari (CISPA Helmholtz Center for Information Security), Giancarlo Pellegrino (CISPA Helmholtz Center for Information Security)

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Secure IP Address Allocation at Cloud Scale

Eric Pauley (University of Wisconsin–Madison), Kyle Domico (University of Wisconsin–Madison), Blaine Hoak (University of Wisconsin–Madison), Ryan Sheatsley (University of Wisconsin–Madison), Quinn Burke (University of Wisconsin–Madison), Yohan Beugin (University of Wisconsin–Madison), Engin Kirda (Northeastern University), Patrick McDaniel (University of Wisconsin–Madison)

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Non-intrusive and Unconstrained Keystroke Inference in VR Platforms via...

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)

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