---
author:
- Tianming Liu
- Haoyu Wang
- Li Li
- Xiapu Luo
- Feng Dong
- Yao Guo
- Liu Wang
- 'Tegawend<span>é</span> F. Bissyand<span>é</span>'
- Jacques Klein
bibliography:
- 'cite.bib'
title: 'MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps'
---

&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\_id&gt;10002978.10003022&lt;/concept\_id&gt; &lt;concept\_desc&gt;Security and privacy Software and application security&lt;/concept\_desc&gt; &lt;concept\_significance&gt;500&lt;/concept\_significance&gt; &lt;/concept&gt; &lt;concept&gt; &lt;concept\_id&gt;10002951.10003260.10003272&lt;/concept\_id&gt; &lt;concept\_desc&gt;Information systems Online advertising&lt;/concept\_desc&gt; &lt;concept\_significance&gt;500&lt;/concept\_significance&gt; &lt;/concept&gt; &lt;concept&gt; &lt;concept\_id&gt;10003120.10003138&lt;/concept\_id&gt; &lt;concept\_desc&gt;Human-centered computing Ubiquitous and mobile computing&lt;/concept\_desc&gt; &lt;concept\_significance&gt;500&lt;/concept\_significance&gt; &lt;/concept&gt; &lt;/ccs2012&gt;

Conclusion
==========

In this paper, we perform a large-scale characterization study of mobile ad content, which has been largely overlooked by the research community. We first create a comprehensive categorization of devious mobile ad contents, then we build <span>*MadDroid*</span>, a framework for automated detection of devious mobile ad contents. By applying <span>*MadDroid*</span> to 40,000 Android apps, we find that devious ad contents are prevalent: 6% of apps in our study are identified as delivering devious ad contents. To the best of our knowledge, <span>*MadDroid*</span> is the first attempt towards mitigating threats from both ad-load and ad-click introduced by mobile ad contents.

Acknowledgment {#acknowledgment .unnumbered}
==============

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.

{'source': 'arXiv_out/2002'}