# MadDroid: Characterising and Detecting Devious Ad Content for Android Apps

arXiv: https://arxiv.org/abs/2002.01656 · HTML: https://mlsanity.com/paper/2002.01656/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [AidUI: Toward Automated Recognition of Dark Patterns in User Interfaces](https://mlsanity.com/paper/2303.06782/) (2023)
- [Agent+P: Guiding UI Agents via Symbolic Planning](https://mlsanity.com/paper/2510.06042/) (2025)
- [From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps](https://mlsanity.com/paper/2411.18084/) (2024)
- [Measuring the Security of Mobile LLM Agents under Adversarial Prompts from Untrusted Third-Party Channels](https://mlsanity.com/paper/2510.27140/) (2025)
- [MANA: Towards Efficient Mobile Ad Detection via Multimodal Agentic UI Navigation](https://mlsanity.com/paper/2603.20351/) (2026)

## Abstract (arXiv)

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.