Abstract
With the widespread application of Android smartphones, privacy protection plays a crucial role. Android vault application provides content hiding on personal terminals to protect user privacy. However, some vault applications do not achieve real privacy protection, and its camouflage ability can be maliciously used to hide illegal information to avoid forensics. In order to solve these two issues, behavior analysis is conducted to compare three aspects of typical vaults in the third-party market. The conclusions and recommendations were given. Support Vector Machine (SVM) was used to distinguish vault from normal applications. Extensive experiments show that SVM can achieve 93.33% classification accuracy rate.
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Acknowledgement
This work was supported in part by the 13th Five-Year Science and Technology Research Project of the Education Department of Jilin Province under Grant No. JJKH20200794KJ, the Innovation Fund of Changchun University of Science and Technology under Grant No. XJJLG-2018-09, the fund of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education (Jilin University) under Grant No. 93K172018K05.
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Xie, N., Bai, H., Sun, R., Di, X. (2020). Android Vault Application Behavior Analysis and Detection. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_31
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DOI: https://doi.org/10.1007/978-981-15-7981-3_31
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