  Advertising click fraud detection plays one of the vital roles in current
E-commerce websites as advertising is an essential component of its business
model. It aims at, given a set of corresponding features, e.g., demographic
information of users and statistical features of clicks, predicting whether a
click is fraudulent or not in the community. Recent efforts attempted to
incorporate attributed behavior sequence and heterogeneous network for
extracting complex features of users and achieved significant effects on click
fraud detection. In this paper, we propose a Multimodal and Contrastive
learning network for Click Fraud detection (MCCF). Specifically, motivated by
the observations on differences of demographic information, behavior sequences
and media relationship between fraudsters and genuine users on E-commerce
platform, MCCF jointly utilizes wide and deep features, behavior sequence and
heterogeneous network to distill click representations. Moreover, these three
modules are integrated by contrastive learning and collaboratively contribute
to the final predictions. With the real-world datasets containing 2.54 million
clicks on Alibaba platform, we investigate the effectiveness of MCCF. The
experimental results show that the proposed approach is able to improve AUC by
7.2% and F1-score by 15.6%, compared with the state-of-the-art methods.
