Mobile advertisement click fraud detection method and system, terminal and medium

Abstract

The invention provides a mobile advertisement click fraud detection method and system, and the method comprises the steps: converting a click event for an advertisement in an app into a data dependence graph, further converting the data dependence graph into a feature vector, carrying out the reconstruction of the feature vector through a variational auto-encoder, judging whether the click event is a one-time click fraud event or not through a reconstruction error, and positioning the code position where the click fraud occurs, so that related personnel can investigate and obtain evidences of app click fraud behaviors. Meanwhile, the invention provides a click fraud detection system and a terminal which are used for executing the mobile advertisement click fraud detection method based on static detection. Meanwhile, the invention provides a corresponding terminal and a medium. According to the method, the static detection technology and the one class classification algorithm are utilized, manual intervention is not needed, a large number of click fraud samples with labels in the early stage are not needed either, more efficient, rapid and flexible click fraud detection can be achieved, and the requirements of related industries are met.

Description

technical field [0001] The present invention relates to the technical field of static detection, in particular to a method, system, terminal and medium for detecting click fraud of mobile advertisements based on static detection. Background technique [0002] For decades, with the unprecedented popularity of smartphones, in order to incentivize application developers to embed advertisers' advertisements in their applications, the pay-per-click (PPC) mechanism has been widely deployed, in which advertisers are rewarded by users based on embedded advertisements. The number of clicks pays the developer. However, PPC mechanisms also face the growing threat of click fraud. Using click fraud tactics, some developers use code to generate "fake" ad clicks that don't come from real users in order to generate additional revenue from ad networks. For example, attackers can embed malicious code on fraudulent apps or third-party SDKs that can be exploited by other unsuspecting app deve...

AI Technical Summary

Problems solved by technology

[0007] 1. No appropriate features are defined at the decompiled code level, so the difference between fake click events and normal click events cannot be reflected

[0008] 2. These features cannot be extracted automatically

[0009] 3. It is impossible to accurately distinguish between fake click events and normal click events under the premise of limited understanding of malicious samples

[0010] At present, there is no description or report of the similar technology of the present invention, and no similar data at home and abroad have been collected yet.

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