A Transfer Learning Framework Towards Identifying Behavioral Changes of Fraudulent Publishers in Pay-Per-Click Model of Online Advertising for Click Fraud Detection

Abstract

The absence of a publicly available user-click dataset makes the task of fraudster identification particularly challenging to detect click fraud in online advertising. However, the task becomes more complicated with concept drift, where a publisher appears in distinct sets with the same pattern but differs in real status labels. Fraud-detection algorithms developed the actual status labels. The reliability of the predictions made by learning models needs to be examined to deal with the concept drift concerning the changing behaviour of fraudulent publishers without re-training the predictive model from scratch. However, the scarcity of pre-trained models aggravates the issue. Thus, we proposed a 1D-convolutional neural network-based fraudsters identification network (FINet) that deals with such challenges using a model trained on a correlated prediction modelling problem using transfer learning. To overcome the scarcity of pre-trained models, we designed a model FINet trained on the correlated dataset TalkingData Ad Tracking Fraud Detection (TDA) and utilized the weights of the trained model to learn a model on the FDMA2012 dataset better. This predicted modelling on a distinct but related problem is re-used for accelerating the training and improving the FINet’s fraudster identification performance. The proposed FINet’s behaviour is investigated based on eight optimizer algorithms in terms of Average Precision, Recall, F1-score, and AUC scores and performance generalization is verified by conducting experiments with existing state-of-art deep learning models. The implementation results are notably higher than the existing state-of-the-art deep learning models and exhibit effective performance towards fraudster’s identification in detecting click fraud