@Article{cmc.2020.09853,
AUTHOR = {Jiaye Pan, Yi Zhuang, Xinwen Hu, Wenbing Zhao},
TITLE = {Fine-Grained Binary Analysis Method for Privacy Leakage Detection on the Cloud Platform},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {607--622},
URL = {http://www.techscience.com/cmc/v64n1/39162},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.09853}
}