Fine-Grained Binary Analysis Method for Privacy Leakage Detection on the Cloud Platform |
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Authors: | Jiaye Pan Yi Zhuang Xinwen Hu Wenbing Zhao |
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Affiliation: | 1.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 200016, China.
2 School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
3 Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, 44115-2214, USA. |
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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. |
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Keywords: | Cloud platform privacy leakage binary analysis dynamic analysis |
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