How do Mobile Apps Violate the Behavioral Policy of Advertisement Libraries?

Author:

Dong Feng1,Wang Haoyu2,Li Li3,Guo Yao4,Xu Guoai1,Zhang Shaodong1

Affiliation:

1. Beijing University of Posts and Telecommunications, Beijing, China

2. Beijing University of Posts and Telecommunications & Beijing Key Lab. of Intelligent Telecomm. Software and Multimedia, Beijing, China

3. Monash University, Melbourne, Australia

4. Peking University & Key Laboratory of High-Confidence Software Technologies (Ministry of Education), Beijing, China

Publisher

ACM

Reference21 articles.

1. MobiGUITAR: Automated Model-Based Testing of Mobile Apps

2. AppBrain 2017. Number of Android applications. (2017). showURL%https://www.appbrain.com/stats/number-of-android-apps AppBrain 2017. Number of Android applications. (2017). showURL%https://www.appbrain.com/stats/number-of-android-apps

3. Geumhwan Cho Junsung Cho Youngbae Song and Hyoungshick Kim 2015. An empirical study of click fraud in mobile advertising networks ARES. IEEE 382--388. 10.1109/ARES.2015.62 Geumhwan Cho Junsung Cho Youngbae Song and Hyoungshick Kim 2015. An empirical study of click fraud in mobile advertising networks ARES. IEEE 382--388. 10.1109/ARES.2015.62

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