Resourceful And Employable Click Fraud Identification For Handheld Applications

Abstract

Mobile advertising plays a vital role in the mobile app
ecosystem. A major threat to the sustainability of this
ecosystem is click fraud, i.e., ad clicks performed by
malicious code or automatic bot problems. Existing
click fraud detection approaches focus on analyzing
the ad requests at the server side. However, such
approaches may suffer from high false negatives since
the detection can be easily circumvented, e.g., when
the clicks are behind proxies or globally distributed.
In this paper, we present Adsherlock, an efficient and
deployable click fraud detection approach at the client
side (inside the application) for mobile apps.
AdSherlock splits the computation-intensive
operations of click request identification into an offline
procedure and an online procedure. In the offline
procedure, Adsherlock generates both exact patterns
and probabilistic patterns based on URL (Uniform
Resource Locator) tokenization. These patterns are
used in the online procedure for click request
identification and further used for click fraud detection
together with an ad request tree model. We implement
a prototype of AdSherlock and evaluate its
performance using real apps. The online detector is
injected into the app executable archive through
binary instrumentation. Results show that AdSherlock
achieves higher click fraud detection accuracy
compared with state of the art, with negligible runtime
overhead