标题:MonkeyDroid: Detecting Unreasonable Privacy Leakages of Android Applications
作者:Ma, Kai; Liu, Mengyang; Guo, Shanqing; Ban, Tao
通讯作者:Ma, Kai
作者机构:[Ma, Kai; Liu, Mengyang; Guo, Shanqing] Shandong Univ, Dept Comp Sci & Technol, Jinan 250100, Peoples R China.; [Ban, Tao] NICT, Tokyo, Japan.
会议名称:22nd International Conference on Neural Information Processing (ICONIP)
会议日期:NOV 09-12, 2015
来源:NEURAL INFORMATION PROCESSING, PT III
出版年:2015
卷:9491
页码:384-391
DOI:10.1007/978-3-319-26555-1_43
关键词:Android security; Privacy leakage detection; Static taint analysis;; Natural language processing
摘要:Static and dynamic taint-analysis approaches have been developed to detect the processing of sensitive information. Unfortunately, faced with the result of analysis about operations of sensitive information, people have no idea of which operation is legitimate operation and which is stealthy malicious behavior. In this paper, we present Monkeydroid to pinpoint automatically whether the android application would leak sensitive information of users by distinguishing the reasonable and unreasonable operation of sensitive information on the basis of information provided by developer and market provider. We evaluated Monkeydroid over the top 500 apps on the Google play and experiments show that our tool can effectively distinguish malicious operations of sensitive information from legitimate ones.
收录类别:CPCI-S;EI;SCOPUS
Scopus被引频次:2
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951984297&doi=10.1007%2f978-3-319-26555-1_43&partnerID=40&md5=d93c16dcf31f78bcdaa6950c748f2fe4
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