Abstract
Mobile devices, such as Android and iOS devices, are an attractive target for cyber-criminals, due to the amount of private data that can be accessed or stored on such devices. As public charging facilities become more commonplace, phone charging attacks are no longer fiction. Juice filming charging (JFC) attack is a particular phone charging threat, which can capture or infer users’ private information by automatically recording screen information from mobile devices during the entire charging process. In this work, we first investigate the types of phone applications users would interact with the most when charging their devices. Then, we propose a detection approach for JFC attacks solely by analyzing CPU usage. In the evaluation, we collect data from a total of 187 participants, and the findings show that our approach using the SVM classifier can achieve better performance than other approaches. Our work complements existing security mechanisms against charging threats.
| Original language | English |
|---|---|
| Journal | Computers and Electrical Engineering |
| Volume | 78 |
| Pages (from-to) | 230-241 |
| ISSN | 0045-7906 |
| DOIs | |
| Publication status | Published - 1 Sep 2019 |
Keywords
- Android and iOS security
- Charging threat
- CPU usage analysis
- JFC attack
- Supervised machine learning
Cite this
}
Towards detection of juice filming charging attacks via supervised CPU usage analysis on smartphones. / Meng, Weizhi; Jiang, Lijun; Choo, Kim Kwang Raymond; Wang, Yu; Jiang, Chong.
In: Computers and Electrical Engineering, Vol. 78, 01.09.2019, p. 230-241.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - Towards detection of juice filming charging attacks via supervised CPU usage analysis on smartphones
AU - Meng, Weizhi
AU - Jiang, Lijun
AU - Choo, Kim Kwang Raymond
AU - Wang, Yu
AU - Jiang, Chong
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Mobile devices, such as Android and iOS devices, are an attractive target for cyber-criminals, due to the amount of private data that can be accessed or stored on such devices. As public charging facilities become more commonplace, phone charging attacks are no longer fiction. Juice filming charging (JFC) attack is a particular phone charging threat, which can capture or infer users’ private information by automatically recording screen information from mobile devices during the entire charging process. In this work, we first investigate the types of phone applications users would interact with the most when charging their devices. Then, we propose a detection approach for JFC attacks solely by analyzing CPU usage. In the evaluation, we collect data from a total of 187 participants, and the findings show that our approach using the SVM classifier can achieve better performance than other approaches. Our work complements existing security mechanisms against charging threats.
AB - Mobile devices, such as Android and iOS devices, are an attractive target for cyber-criminals, due to the amount of private data that can be accessed or stored on such devices. As public charging facilities become more commonplace, phone charging attacks are no longer fiction. Juice filming charging (JFC) attack is a particular phone charging threat, which can capture or infer users’ private information by automatically recording screen information from mobile devices during the entire charging process. In this work, we first investigate the types of phone applications users would interact with the most when charging their devices. Then, we propose a detection approach for JFC attacks solely by analyzing CPU usage. In the evaluation, we collect data from a total of 187 participants, and the findings show that our approach using the SVM classifier can achieve better performance than other approaches. Our work complements existing security mechanisms against charging threats.
KW - Android and iOS security
KW - Charging threat
KW - CPU usage analysis
KW - JFC attack
KW - Supervised machine learning
U2 - 10.1016/j.compeleceng.2019.07.008
DO - 10.1016/j.compeleceng.2019.07.008
M3 - Journal article
VL - 78
SP - 230
EP - 241
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
SN - 0045-7906
ER -