Dissecting Click Fraud Autonomy in the Wild
Tong Zhu, Yan Meng, Haotian Hu, Xiaokuan Zhang, Minhui Xue, Haojin Zhu

TL;DR
This paper introduces ClickScanner, a static analysis tool using VAE to detect a novel humanoid click fraud attack in Android apps, revealing its prevalence and attack patterns at large scale.
Contribution
The study presents the first static analysis-based detection method for humanoid click fraud and provides large-scale measurement results across major app markets.
Findings
Humanoid attacks are prevalent even in top-rated apps.
Ad SDK-based attacks are now dominant.
Attack patterns vary across app categories and popularity.
Abstract
Although the use of pay-per-click mechanisms stimulates the prosperity of the mobile advertisement network, fraudulent ad clicks result in huge financial losses for advertisers. Extensive studies identify click fraud according to click/traffic patterns based on dynamic analysis. However, in this study, we identify a novel click fraud, named humanoid attack, which can circumvent existing detection schemes by generating fraudulent clicks with similar patterns to normal clicks. We implement the first tool ClickScanner to detect humanoid attacks on Android apps based on static analysis and variational AutoEncoder (VAE) with limited knowledge of fraudulent examples. We define novel features to characterize the patterns of humanoid attacks in the apps' bytecode level. ClickScanner builds a data dependency graph (DDG) based on static analysis to extract these key features and form a feature…
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