MagDefender: Detecting Eavesdropping on Mobile Devices using the Built-in Magnetometer

This study reveals that on-board hardware modules leak electromagnetic (EM) emissions whenever audio or camera data is accessed, and proposes Magdefender scheme that explores the possibility of using the magnetometer built into mobile devices to detect eavesdropping instances by malicious apps and even the unscrupulous phone vendors. However, the target EM signals generated by accessing multimedia data is weak and tends to be buried beneath other noisy EM signals from apps running in the foreground. It is also subject to the external interference from geomagnetic signals generated by the device movement. To cope with the challenges, we adopt a generative adversarial networks (GAN) based model to facilitate the extraction of target EM signals indicating the occurrence of eavesdropping from the overall magnetometer readings. We also develop a neural network-based classifier with triplet loss embedding to identify the EM signals from the camera and/or microphones. Empirical results demonstrate the efficacy of MagDefenderin recognizing instances of eavesdropping on cameras/microphones data, with average accuracy of 97.3% when applied to the trained devices, and average 91.5% on unseen mobile devices.

In 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)