Reference : MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/45320
MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps
English
Liu, Tianming [Beijing University of Posts and Telecommunications]
Wang, Haoyu [Beijing University of Posts and Telecommunications]
Li, Li [Monash University]
Luo, Xiapu [The Hong Kong Polytechnic University]
Dong, Feng [Beijing University of Posts and Telecommunications]
Guo, Yao [Peking University]
Wang, Liu [Beijing University of Posts and Telecommunications]
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Klein, Jacques mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Apr-2020
Proceedings of The Web Conference 2020
Association for Computing Machinery
WWW '20
1715–1726
Yes
International
9781450370233
New York, NY, USA
Proceedings of The Web Conference 2020 (WWW)
April 2020
Taipei, Taiwan
[en] ad fraud ; mobile advertising ; Android app ; malware
[en] Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable contents. To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing. In this work, we first provide a comprehensive categorization of devious ad contents, including five kinds of behaviors belonging to two categories: ad loading content and ad clicking content. Then, we propose MadDroid, a framework for automated detection of devious ad contents. MadDroid leverages an automated app testing framework with a sophisticated ad view exploration strategy for effectively collecting ad-related network traffic and subsequently extracting ad contents. We then integrate dedicated approaches into the framework to identify devious ad contents. We have applied MadDroid to 40,000 Android apps and found that roughly 6% of apps deliver devious ad contents, e.g., distributing malicious apps that cannot be downloaded via traditional app markets. Experiment results indicate that devious ad contents are prevalent, suggesting that our community should invest more effort into the detection and mitigation of devious ads towards building a trustworthy mobile advertising ecosystem.
http://hdl.handle.net/10993/45320
10.1145/3366423.3380242
https://doi.org/10.1145/3366423.3380242
FnR ; FNR11693861 > Jacques Klein > CHARACTERIZE > Characterization of Malicious Code in Mobile Apps: Towards Accurate and Explainable Malware Detection > 01/06/2018 > 31/05/2021 > 2017

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