Towards detection of juice filming charging attacks via supervised CPU usage analysis on smartphones. (September 2019)

• Record Type: Journal Article
• Authors: Meng, Weizhi; Jiang, Lijun; Choo, Kim-Kwang Raymond; Wang, Yu; Jiang, Chong
• Is Part Of: Computers & electrical engineering. Volume 78(2019)
• Page Start: 230
• Page End: 241
• Publication Date: 2019-09
• Subjects: Charging threat -- Android and iOS security -- CPU usage analysis -- JFC attack -- Supervised machine learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.07.008
• Languages: English
• ISSNs: 0045-7906

Abstract

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.