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2021 | OriginalPaper | Buchkapitel

HIAWare: Speculate Handwriting on Mobile Devices with Built-In Sensors

verfasst von : Jing Chen, Peidong Jiang, Kun He, Cheng Zeng, Ruiying Du

Erschienen in: Information and Communications Security

Verlag: Springer International Publishing

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Abstract

A variety of sensors are built into intelligent mobile devices. However, these sensors can be used as side channels for inferring information. Researchers have shown that some touchscreen information, such as PIN and unlock pattern, can be speculated by background applications with motion sensors. Those attacks mainly focus on the restricted-area input interface (e.g., virtual keyboard). To date, the privacy risk in the unrestricted-area input interface does not receive sufficient attention.
In this paper, we investigate such privacy risk and design an unrestricted-area information speculation framework, called Handwritten Information Awareness (HIAWare). HIAWare exploits the sensors’ signals that are affected by handwriting actions to speculate the handwritten characters. To alleviate the impact of different handwriting habits, we utilize the generality patterns of characters. Furthermore, to mitigate the impact of holding posture in handwriting, we propose a user-independent posture-aware approach. As a result, HIAWare can attack any victim without obtaining the victim’s information in advance. The experiments show that the speculation accuracy of HIAWare is close to 90.0%, demonstrating the viability of HIAWare.

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Literatur
2.
Zurück zum Zitat Aviv, A.J., Sapp, B., Blaze, M., Smith, J.M.: Practicality of accelerometer side channels on smartphones. In: Proceedings of ACSAC, pp. 41–50 (2012) Aviv, A.J., Sapp, B., Blaze, M., Smith, J.M.: Practicality of accelerometer side channels on smartphones. In: Proceedings of ACSAC, pp. 41–50 (2012)
3.
Zurück zum Zitat Cai, L., Chen, H.: Touchlogger: inferring keystrokes on touch screen from smartphone motion. In: Proceedings of HotSec (2011) Cai, L., Chen, H.: Touchlogger: inferring keystrokes on touch screen from smartphone motion. In: Proceedings of HotSec (2011)
4.
Zurück zum Zitat Chen, D., et al.: Magleak: a learning-based side-channel attack for password recognition with multiple sensors in IIoT environment. IEEE Trans. Ind. Inform. (2020) Chen, D., et al.: Magleak: a learning-based side-channel attack for password recognition with multiple sensors in IIoT environment. IEEE Trans. Ind. Inform. (2020)
5.
Zurück zum Zitat Chen, J., Fang, Y., He, K., Du, R.: Charge-depleting of the batteries makes smartphones recognizable. In: Proceedings of ICPADS, pp. 33–40 (2017) Chen, J., Fang, Y., He, K., Du, R.: Charge-depleting of the batteries makes smartphones recognizable. In: Proceedings of ICPADS, pp. 33–40 (2017)
6.
Zurück zum Zitat Chen, Y., Jin, X., Sun, J., Zhang, R., Zhang, Y.: POWERFUL: mobile app fingerprinting via power analysis. In: Proceedings of INFOCOM, pp. 1–9 (2017) Chen, Y., Jin, X., Sun, J., Zhang, R., Zhang, Y.: POWERFUL: mobile app fingerprinting via power analysis. In: Proceedings of INFOCOM, pp. 1–9 (2017)
7.
Zurück zum Zitat Chen, Z., Zhu, Q., Soh, Y.C., Zhang, L.: Robust human activity recognition using smartphone sensors via CT-PCA and Misc SVM. IEEE Trans. Ind. Inform. 13(6), 3070–3080 (2017)CrossRef Chen, Z., Zhu, Q., Soh, Y.C., Zhang, L.: Robust human activity recognition using smartphone sensors via CT-PCA and Misc SVM. IEEE Trans. Ind. Inform. 13(6), 3070–3080 (2017)CrossRef
8.
Zurück zum Zitat Du, H., Li, P., Zhou, H., Gong, W., Luo, G., Yang, P.: WordRecorder: accurate acoustic-based handwriting recognition using deep learning. In: Proceedings of INFOCOM, pp. 1448–1456 (2018) Du, H., Li, P., Zhou, H., Gong, W., Luo, G., Yang, P.: WordRecorder: accurate acoustic-based handwriting recognition using deep learning. In: Proceedings of INFOCOM, pp. 1448–1456 (2018)
12.
Zurück zum Zitat Mehrnezhad, M., Toreini, E., Shahandashti, S.F., Hao, F.: Touchsignatures: identification of user touch actions and pins based on mobile sensor data via javascript. J. Inf. Sec. Appl. 26, 23–38 (2016) Mehrnezhad, M., Toreini, E., Shahandashti, S.F., Hao, F.: Touchsignatures: identification of user touch actions and pins based on mobile sensor data via javascript. J. Inf. Sec. Appl. 26, 23–38 (2016)
13.
Zurück zum Zitat Ping, D., Sun, X., Mao, B.: TextLogger: inferring longer inputs on touch screen using motion sensors. In: Proceedings of WiSec, pp. 24:1–24:12 (2015) Ping, D., Sun, X., Mao, B.: TextLogger: inferring longer inputs on touch screen using motion sensors. In: Proceedings of WiSec, pp. 24:1–24:12 (2015)
15.
Zurück zum Zitat Qin, Y., Yue, C.: Website fingerprinting by power estimation based side-channel attacks on Android 7. In: Proceedings of TrustCom, pp. 1030–1039 (2018) Qin, Y., Yue, C.: Website fingerprinting by power estimation based side-channel attacks on Android 7. In: Proceedings of TrustCom, pp. 1030–1039 (2018)
16.
Zurück zum Zitat Quispe, K.G.M., Lima, W.S., Batista, D.M., Souto, E.: MBOSS: a symbolic representation of human activity recognition using mobile sensors. Sensors 18(12), 4354 (2018)CrossRef Quispe, K.G.M., Lima, W.S., Batista, D.M., Souto, E.: MBOSS: a symbolic representation of human activity recognition using mobile sensors. Sensors 18(12), 4354 (2018)CrossRef
18.
Zurück zum Zitat Spreitzer, R., Moonsamy, V., Korak, T., Mangard, S.: Systematic classification of side-channel attacks: a case study for mobile devices. IEEE Commun. Surv. Tutorials 20(1), 465–488 (2018)CrossRef Spreitzer, R., Moonsamy, V., Korak, T., Mangard, S.: Systematic classification of side-channel attacks: a case study for mobile devices. IEEE Commun. Surv. Tutorials 20(1), 465–488 (2018)CrossRef
19.
Zurück zum Zitat Spreitzer, R., Kirchengast, F., Gruss, D., Mangard, S.: ProcHarvester: fully automated analysis of procfs side-channel leaks on Android. In: Proceedings of ASIACCS, pp. 749–763 (2018) Spreitzer, R., Kirchengast, F., Gruss, D., Mangard, S.: ProcHarvester: fully automated analysis of procfs side-channel leaks on Android. In: Proceedings of ASIACCS, pp. 749–763 (2018)
20.
Zurück zum Zitat Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Patt. Recogn. Lett. 119, 3–11 (2019)CrossRef Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Patt. Recogn. Lett. 119, 3–11 (2019)CrossRef
21.
Zurück zum Zitat Xu, Z., Bai, K., Zhu, S.: Taplogger: inferring user inputs on smartphone touchscreens using on-board motion sensors. In: Proceedings of WiSec, pp. 113–124 (2012) Xu, Z., Bai, K., Zhu, S.: Taplogger: inferring user inputs on smartphone touchscreens using on-board motion sensors. In: Proceedings of WiSec, pp. 113–124 (2012)
22.
Zurück zum Zitat Yu, T., Jin, H., Nahrstedt, K.: Writinghacker: audio based eavesdropping of handwriting via mobile devices. In: Proceedings of UbiComp, pp. 463–473 (2016) Yu, T., Jin, H., Nahrstedt, K.: Writinghacker: audio based eavesdropping of handwriting via mobile devices. In: Proceedings of UbiComp, pp. 463–473 (2016)
23.
Zurück zum Zitat Zhao, R., Yue, C., Han, Q.: Sensor-based mobile web cross-site input inference attacks and defenses. IEEE Trans. Inf. Forensics Secur. 14(1), 75–89 (2019)CrossRef Zhao, R., Yue, C., Han, Q.: Sensor-based mobile web cross-site input inference attacks and defenses. IEEE Trans. Inf. Forensics Secur. 14(1), 75–89 (2019)CrossRef
24.
Zurück zum Zitat Zhou, M., Wang, Q., Yang, J., Li, Q., Xiao, F., Wang, Z., Chen, X.: Patternlistener: cracking android pattern lock using acoustic signals. In: Proceedings of CCS, pp. 1775–1787 (2018) Zhou, M., Wang, Q., Yang, J., Li, Q., Xiao, F., Wang, Z., Chen, X.: Patternlistener: cracking android pattern lock using acoustic signals. In: Proceedings of CCS, pp. 1775–1787 (2018)
Metadaten
Titel
HIAWare: Speculate Handwriting on Mobile Devices with Built-In Sensors
verfasst von
Jing Chen
Peidong Jiang
Kun He
Cheng Zeng
Ruiying Du
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-86890-1_8

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