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Home > Archive>Volume 28, Issue 6, 2017 >1373-1388. DOI:10.13328/j.cnki.jos.005221
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Automated Detection and Classification of Third-Party Libraries in Large Scale Android Apps
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National Natural Science Foundation of China (61421061, 61421091); National High Technology Research and Development Program of China (863) (2015AA017202)

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    Abstract:

    Third-Party libraries are widely used in mobile applications such as Android apps. Much research on app analysis or access control needs to detect or classify third-party libraries first in order to provide accurate results. Most previous studies use a whitelist to identify third-party libraries and manually categorize them. However, it is impossible to build a complete whitelist of third-party libraries and classify them because:(1) there are too many of them; and (2) common techniques such as library obfuscation and library masquerading cannot be handled with a whitelist. In this paper, an automated approach is proposed to detect and classify frequently-used third-party libraries in Android apps. A multi-level clustering based method is presented to identify third-party libraries, and a machine learning based technique is applied to classify the libraries. Experiments on more than 130 000 apps show that 4 916 third-party libraries can be detected without prior knowledge. The classification result of 10-folds cross validation on sampled libraries is 84.28%. With the trained classifier, the proposed approach is able to classify more than 75% of the 4 916 libraries into six categories with an accuracy of 75%.

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王浩宇,郭耀,马子昂,陈向群.大规模移动应用第三方库自动检测和分类方法.软件学报,2017,28(6):1373-1388

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  • Received:May 08,2016
  • Revised:September 09,2016
  • Online: February 21,2017
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