一句话：提出混合分析的 FraudDroid，动态构建 UI 状态转移图并结合运行时网络流量启发式规则，检测 Android 应用中的动态广告欺诈。
移动广告欺诈广泛存在，但已有工作主要针对仅涉及单一 UI 状态的静态布局欺诈（依据广告视图尺寸、位置等静态信息识别），对用户交互过程中跨多个 UI 状态发生的动态交互欺诈尚缺乏研究。本文系统调研移动广告欺诈并提出完整分类体系，进而设计 FraudDroid：动态运行应用以构建 UI 状态转移图，采集对应的运行时网络流量，再依据一组启发式规则判定欺诈行为。实验表明其精确率约 93%、召回率约 92%，可覆盖各类欺诈类型；对 12,000 个含广告 Android 应用的分析共发现 335 例欺诈、涉及 20 个广告网络，均经确认为真阳性。
Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (∼ 93%) and recall (∼ 92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detection.
@inproceedings{paperbot4662,
title = {FraudDroid: automated ad fraud detection for Android apps},
author = {Feng Dong and Haoyu Wang and Li Li and Yao Guo and Tegawendé F. Bissyandé and Tianming Liu and Guoai Xu and Jacques Klein},
booktitle = {FSE},
year = {2018},
doi = {10.1145/3236024.3236045}
}