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.

View More Papers

PQConnect: Automated Post-Quantum End-to-End Tunnels

Daniel J. Bernstein (University of Illinois at Chicago and Academia Sinica), Tanja Lange (Eindhoven University of Technology amd Academia Sinica), Jonathan Levin (Academia Sinica and Eindhoven University of Technology), Bo-Yin Yang (Academia Sinica)

Read More

What’s Done Is Not What’s Claimed: Detecting and Interpreting...

Chang Yue (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Zhixiu Guo (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China), Jun Dai, Xiaoyan Sun (Department of Computer Science, Worcester Polytechnic Institute), Yi Yang (Institute of Information Engineering, Chinese Academy…

Read More

BrowserFM: A Feature Model-based Approach to Browser Fingerprint Analysis

Maxime Huyghe (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Clément Quinton (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Walter Rudametkin (Univ. Rennes, Inria, CNRS, UMR 6074 IRISA)

Read More

LADDER: Multi-Objective Backdoor Attack via Evolutionary Algorithm

Dazhuang Liu (Delft University of Technology), Yanqi Qiao (Delft University of Technology), Rui Wang (Delft University of Technology), Kaitai Liang (Delft University of Technology), Georgios Smaragdakis (Delft University of Technology)

Read More