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P2 DaPanda: Detecting Aggressive Push Notifications in Android Apps

DaPanda:检测 Android 应用中的侵扰性推送通知
ASE 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)2019匹配度 0/10 · 该论文研究 Android 应用侵扰性推送通知的检测,属于移动安全/隐私与动态测试方向,未涉及用户关注的程序语言、静态分析、LLM/Agent、浮点程序分析或贝叶斯程序分析。引用 28 inbox
Tianming Liu, Haoyu Wang, Li Li, Guangdong Bai, Yao Guo, Guoai Xu
crossref: 2026-09-12

AI 速览

一句话:首次系统研究并自动检测 Android 应用中的侵扰性推送通知,并在 2 万应用上验证。

论文关注移动推送通知被滥用于广告骚扰等侵扰行为,但此前缺乏系统研究。作者先给出推送通知分类法,并通过众包识别侵扰性通知。提出 DaPanda,一种混合方法:用引导式测试系统性触发并记录推送通知,结合 Android framework 插桩收集通知相关运行时信息,自动判定侵扰性通知。在 20,000 个来自不同应用市场的 Android 应用上评估,检出 1,000 多条侵扰性通知且均经确认。分析表明该类通知跨市场普遍存在,并出现在推送通知生命周期各阶段,需社区采取检测与缓解措施。

问题
移动推送通知可被用于广告骚扰等侵扰目的,但社区此前缺乏对侵扰性推送通知的系统检测与刻画。
方法
提出 DaPanda,先用众包建立分类并识别侵扰性通知,再通过引导式测试触发通知,并对 Android framework 插桩收集运行时信息进行自动检测。
评估
在 20,000 个来自不同市场的 Android 应用上运行,检出 1,000 多条经确认的侵扰性通知,并分析其跨市场与生命周期分布。
原文摘要(Abstract)

Mobile push notifications have been widely used in mobile platforms to deliver all sorts of information to app users. Although it offers great convenience for both app developers and mobile users, this feature was frequently reported to serve malicious and aggressive purposes, such as delivering annoying push notification advertisement. However, to the best of our knowledge, this problem has not been studied by our research community so far. To fill the void, this paper presents the first study to detect aggressive push notifications and further characterize them in the global mobile app ecosystem on a large scale. To this end, we first provide a taxonomy of mobile push notifications and identify the aggressive ones using a crowdsourcing-based method. Then we propose sc DaPanda, a novel hybrid approach, aiming at automatically detecting aggressive push notifications in Android apps. sc DaPanda leverages a guided testing approach to systematically trigger and record push notifications. By instrumenting the Android framework, sc DaPanda further collects all notification-relevant runtime information to flag the aggressive ones. Our experimental results show that sc DaPanda is capable of detecting different types of aggressive push notifications effectively in an automated way. By applying sc DaPanda to 20,000 Android apps from different app markets, it yields over 1,000 aggressive notifications, which have been further confirmed as true positives. Our in-depth analysis further reveals that aggressive notifications are prevalent across different markets and could be manifested in all the phases in the lifecycle of push notifications. It is hence urgent for our community to take actions to detect and mitigate apps involving aggressive push notifications.

链接与引用

DOI 原文 · PDF(开放获取) ·

BibTeX
@inproceedings{paperbot5288,
  title = {DaPanda: Detecting Aggressive Push Notifications in Android Apps},
  author = {Tianming Liu and Haoyu Wang and Li Li and Guangdong Bai and Yao Guo and Guoai Xu},
  booktitle = {ASE},
  year = {2019},
  doi = {10.1109/ase.2019.00017}
}