DeepMag+: Sniffing mobile apps in magnetic field through deep learning

Abstract

This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only 15% and at the same time has a negligible effect on benign Apps.

Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study

“…We called the machine-learning model as a deep learning model when we use multiple layers of neural network. Deep learning model has been used in wide range of applications including computer vision [16][17][18], speech-based medical diagnosis [19,20], cybersecurity [21,22], medical disease diagnosis [19,23], remote sensing [17,24,25] domains. To train a deep learning model from a scratch, we usually need a lot of labeled data because deep learning models contain a large number of trainable parameters.…”