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    Discovering and understanding android sensor usage behaviors with data flow analysis

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    AndroidSensor.pdf
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    1.326Mb
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    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Liu, Xing
    Liu, Jiqiang
    Wang, Wei cc
    He, Yongzhong
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2017-03-20
    Online Publication Date
    2017-03-20
    Print Publication Date
    2018-01
    Permanent link to this record
    http://hdl.handle.net/10754/623822
    
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    Abstract
    Today’s Android-powered smartphones have various embedded sensors that measure the acceleration, orientation, light and other environmental conditions. Many functions in the third-party applications (apps) need to use these sensors. However, embedded sensors may lead to security issues, as the third-party apps can read data from these sensors without claiming any permissions. It has been proven that embedded sensors can be exploited by well designed malicious apps, resulting in leaking users’ privacy. In this work, we are motivated to provide an overview of sensor usage patterns in current apps by investigating what, why and how embedded sensors are used in the apps collected from both a Chinese app. market called “AppChina” and the official market called “Google Play”. To fulfill this goal, We develop a tool called “SDFDroid” to identify the used sensors’ types and to generate the sensor data propagation graphs in each app. We then cluster the apps to find out their sensor usage patterns based on their sensor data propagation graphs. We apply our method on 22,010 apps collected from AppChina and 7,601 apps from Google Play. Extensive experiments are conducted and the experimental results show that most apps implement their sensor related functions by using the third-party libraries. We further study the sensor usage behaviors in the third-party libraries. Our results show that the accelerometer is the most frequently used sensor. Though many third-party libraries use no more than four types of sensors, there are still some third-party libraries registering all the types of sensors recklessly. These results call for more attentions on better regulating the sensor usage in Android apps.
    Citation
    Liu X, Liu J, Wang W, He Y, Zhang X (2017) Discovering and understanding android sensor usage behaviors with data flow analysis. World Wide Web 21: 105–126. Available: http://dx.doi.org/10.1007/s11280-017-0446-0.
    Sponsors
    The work reported in this paper is partially supported by the Fundamental Research funds for the central Universities of China (No. K15JB00190), Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120009120010), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (No. K14C300020), and in part by the 111 Project (B14005).
    Publisher
    Springer Nature
    Journal
    World Wide Web
    DOI
    10.1007/s11280-017-0446-0
    Additional Links
    http://lthlibprod.kaust.edu.sa/publications/forms/processNewItems.php
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11280-017-0446-0
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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