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Smart mobile devices have become an integral part of people's life and users often input sensitive information on these devices. However, various side channel attacks against mobile devices pose a plethora of serious threats against user security and privacy. To mitigate these attacks, we present a novel secure Back-of-Device (BoD) input system, SecTap, for mobile devices. To use SecTap, a user tilts her mobile device to move a cursor on the keyboard and tap the back of the device to secretly input data. We design a tap detection method by processing the stream of accelerometer readings to identify the user's taps in real time. The orientation sensor of the mobile device is used to control the direction and the speed of cursor movement. We also propose an obfuscation technique to randomly and effectively accelerate the cursor movement. This technique not only preserves the input performance but also keeps the adversary from inferring the tapped keys. Extensive empirical experiments were conducted on different smart phones to demonstrate the usability and security on both Android and iOS platforms.
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A Smartphone-Based Cursor Position System in Cross-Device Interaction Using Machine Learning Techniques
The use of mobile devices, especially smartphones, has become popular in recent years. There is an increasing need for cross-device interaction techniques that seamlessly integrate mobile devices and large display devices together. This paper develops a novel cross-device cursor position system that maps a mobile device’s movement on a flat surface to a cursor’s movement on a large display. The system allows a user to directly manipulate objects on a large display device through a mobile device and supports seamless cross-device data sharing without physical distance restrictions. To achieve this, we utilize sound localization to initialize the mobile device position as the starting location of a cursor on the large screen. Then, the mobile device’s movement is detected through an accelerometer and is accordingly translated to the cursor’s movement on the large display using machine learning models. In total, 63 features and 10 classifiers were employed to construct the machine learning models for movement detection. The evaluation results have demonstrated that three classifiers, in particular, gradient boosting, linear discriminant analysis (LDA), and naïve Bayes, are suitable for detecting the movement of a mobile device.
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- Award ID(s):
- 1722913
- PAR ID:
- 10253909
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 21
- Issue:
- 5
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 1665
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s21051665
