iSTELAN: Disclosing Sensitive User Information by Mobile Magnetometer from Finger Touches

iSTELAN: Disclosing Sensitive User Information by Mobile Magnetometer from Finger Touches
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DOI:
10.56553/popets-2023-0042
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发表时间:
2023-04
期刊:
Proc. Priv. Enhancing Technol.
影响因子:
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通讯作者:
Reham Mohamed;Habiba Farrukh;Yi-Wei Lu;He Wang;Z. Berkay Celik
Reham Mohamed;Habiba Farrukh;Yi-Wei Lu;He Wang;Z. Berkay Celik
中科院分区:
其他
文献类型:
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作者:
Reham Mohamed;Habiba Farrukh;Yi-Wei Lu;He Wang;Z. Berkay Celik

文献摘要

相似文献

我们展示了一种新型的侧通道泄漏,其中苹果移动设备中的内置磁力计传感器捕获用户的触摸事件。当人体等导电材料触摸移动设备屏幕时,电流通过屏幕电容器,在触摸点周围产生电磁场。当发生触摸时,无论移动设备静止还是自然握在手中,该电磁场都会导致磁力计信号急剧波动。这些信号可以由在后台运行的移动应用程序访问,无需任何权限。我们开发了 iSTELAN,这是一种三阶段攻击,它利用此侧通道来推断用户的应用程序和触摸数据。 iSTELAN 将磁力计信号转换为二进制序列以揭示用户的触摸事件,利用触摸事件模式来识别用户正在使用的应用程序类型,并对触摸事件进行建模以识别用户在不同应用程序上执行的触摸事件类型。我们在使用 7 种流行应用程序类型的情况下对 22 位用户进行了 iSTELAN 攻击演示,结果表明,该攻击在泄露触摸事件方面的平均准确率达到 90%,在对所使用的应用程序类型进行分类方面达到 74%,在检测触摸事件类型方面达到 73%。
We show a new type of side-channel leakage in which the built-in magnetometer sensor in Apple's mobile devices captures touch events of users. When a conductive material such as the human body touches the mobile device screen, the electric current passes through the screen capacitors generating an electromagnetic field around the touch point. This electromagnetic field leads to a sharp fluctuation in the magnetometer signals when a touch occurs, both when the mobile device is stationary and held in hand naturally. These signals can be accessed by mobile applications running in the background without requiring any permissions. We develop iSTELAN, a three-stage attack, which exploits this side-channel to infer users' application and touch data. iSTELAN translates the magnetometer signals to a binary sequence to reveal users' touch events, exploits touch event patterns to fingerprint the type of application a user is using, and models touch events to identify users' touch event types performed on different applications. We demonstrate the iSTELAN attack on 22 users while using 7 popular app types and show that it achieves an average accuracy of 90% for disclosing touch events, 74% for classifying application type used, and 73% for detecting touch event types.