TY - GEN
T1 - MadDroid
T2 - Characterizing and Detecting Devious Ad Contents for Android Apps
AU - Liu, Tianming
AU - Wang, Haoyu
AU - Li, Li
AU - Luo, Xiapu
AU - Dong, Feng
AU - Guo, Yao
AU - Wang, Liu
AU - Bissyandé, Tegawendé
AU - Klein, Jacques
N1 - Funding Information:
This work was partly supported by the National Natural Science Foundation of China (No.61702045 and No.61772042), by the Hong Kong RGC Projects (No.152223/17E, CityU C1008-16G), by the Australian Research Council (ARC) under projects DE200100016 and DP200100020, by the Fonds National de la Recherche (FNR), Luxembourg, under project CHARACTERIZE C17/IS/11693861, by the SPARTA project which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 830892.
Publisher Copyright:
© 2020 ACM.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - 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.
AB - 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.
KW - Android app
KW - ad fraud
KW - malware
KW - mobile advertising
UR - http://www.scopus.com/inward/record.url?scp=85086566051&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3366423.3380242
DO - https://doi.org/10.1145/3366423.3380242
M3 - Conference article published in proceeding or book
T3 - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
SP - 1715
EP - 1726
BT - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PB - ACM
ER -