Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146886
Title: Fine-grained binary analysis method for privacy leakage detection on the cloud platform
Authors: Pan, Jiaye
Zhuang, Yi
Hu, Xinwen
Zhao, Wenbing
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Pan, J., Zhuang, Y., Hu, X. & Zhao, W. (2020). Fine-grained binary analysis method for privacy leakage detection on the cloud platform. Computers, Materials and Continua, 64(1), 607-622. https://dx.doi.org/10.32604/cmc.2020.09853
Journal: Computers, Materials and Continua
Abstract: Nowadays cloud architecture is widely applied on the internet. New malware aiming at the privacy data stealing or crypto currency mining is threatening the security of cloud platforms. In view of the problems with existing application behavior monitoring methods such as coarse-grained analysis, high performance overhead and lack of applicability, this paper proposes a new fine-grained binary program monitoring and analysis method based on multiple system level components, which is used to detect the possible privacy leakage of applications installed on cloud platforms. It can be used online in cloud platform environments for fine-grained automated analysis of target programs, ensuring the stability and continuity of program execution. We combine the external interception and internal instrumentation and design a variety of optimization schemes to further reduce the impact of fine-grained analysis on the performance of target programs, enabling it to be employed in actual environments. The experimental results show that the proposed method is feasible and can achieve the acceptable analysis performance while consuming a small amount of system resources. The optimization schemes can go beyond traditional dynamic instrumentation methods with better analytical performance and can be more applicable to online analysis on cloud platforms.
URI: https://hdl.handle.net/10356/146886
ISSN: 1546-2218
DOI: 10.32604/cmc.2020.09853
Rights: © 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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