  Fraudulent activities related to online advertising can potentially harm the
trust advertisers put in advertising networks and sour the gaming experience
for users. Pay-Per-Click/Install (PPC/I) advertising is one of the main revenue
models in game monetization. Widespread use of the PPC/I model has led to a
rise in click/install fraud events in games. The majority of traffic in ad
networks is non-fraudulent, which imposes difficulties on machine learning
based fraud detection systems to deal with highly skewed labels. From the ad
network standpoint, user activities are multi-type sequences of temporal events
consisting of event types and corresponding time intervals. Time Long
Short-Term Memory (Time-LSTM) network cells have been proved effective in
modeling intrinsic hidden patterns with non-uniform time intervals. In this
study, we propose using a variant of Time-LSTM cells in combination with a
modified version of Sequence Generative Adversarial Generative (SeqGAN)to
generate artificial sequences to mimic the fraudulent user patterns in ad
traffic. We also propose using a Critic network instead of Monte-Carlo (MC)
roll-out in training SeqGAN to reduce computational costs. The GAN-generated
sequences can be used to enhance the classification ability of event-based
fraud detection classifiers. Our extensive experiments based on synthetic data
have shown the trained generator has the capability to generate sequences with
desired properties measured by multiple criteria.
