Generating multi-type sequences of temporal events to improve fraud detection in game advertising

• URL: http://arxiv.org/abs/2104.03428v1
• Date: Wed, 7 Apr 2021 23:19:13 GMT
• Title: Generating multi-type sequences of temporal events to improve fraud detection in game advertising
• Authors: Lun Jiang, Nima Salehi Sadghiani, Zhuo Tao
• Abstract summary: 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. The GAN-generated sequences can be used to enhance the classification ability of event-based fraud detections.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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