DeepMag: Sniffing Mobile Apps in Magnetic Field through Deep Convolutional Neural Networks

In this paper, we report a newfound vulnerability on smartphones due to the malicious use of unsupervised sensor data. We demonstrate that an attacker can train deep Convolutional Neural Networks (CNN) by using magnetometer or orientation data to effectively infer the Apps and their usage information on a smartphone with an accuracy of over 80%. Furthermore, we show that such attacks can become even worse if sophisticated attackers exploit motion sensors to cluster the magnetometer or orientation data, improving the accuracy 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 negligible effect on benign Apps.

在本文中，我们报告了由于恶意使用无监督传感器数据而在智能手机上发现的一种新漏洞。我们证明，攻击者可以通过使用磁力计或方向数据来训练深度卷积神经网络（CNN），从而以超过80%的准确率有效地推断智能手机上的应用程序及其使用信息。此外，我们还表明，如果狡猾的攻击者利用运动传感器对磁力计或方向数据进行聚类，此类攻击可能会变得更糟，准确率可提高到高达98%。为了缓解此类攻击，我们提出了一种噪声注入方案，该方案可以有效地将应用程序嗅探准确率降低到仅15%，同时对良性应用程序的影响可以忽略不计。