## 8 Taxonomy of Ad Fraud Prevention and Detection Methods
### Detection of Digital Advertising Fraud in Academia
#### 8.2.2 Traffic Fraud Detection  
The most widely used revenue models in digital advertising platforms, the pay-per-click model, is particularly susceptible to the previously discussed form of traffic fraud--click fraud. In [2018], [PERSON] et al. have introduced a multi-modal real-time detection and prevention system to address the problem of click fraud in the online ad system. The proposed model is based on Collaborative Click Fraud Detection and Prevention (CCFDP) system [2018] involving a collaboration between client-side logs and server-side logs. In essence, CCFDP is designed to collect data about each click, including the data fusion between the client-side and server-side logs. CCFDP applies multi-level data to enhance the description of each click and obtain a better estimation of the click traffic quality. [PERSON] et al. have also performed analysis on the extended click records using three independent data mining modules: rule-based, outlier, and click map. The output of each of these modules is a probabilistic measure of evidence that an examined click is fraudulent. Based on the individual scores estimated by the three independent modules, a cumulative score is assigned to each click using the Dempster-Shafer evidence theory. The authors have examined version 1.0 (the initial version (version 1.0) of CCFDP was invented using a rule-based system, which closely matches most of the click fraud detection systems available today) and version 2.0 (the new CCFDP (version 2.0) has an outlier module and the click map module, and an improved rule-based system with additional click context information) of CCFDP systems with data from a real ad campaign. The obtained results have revealed that the use of multi-level data fusion can enhance the overall quality of click fraud analysis. [PERSON] [85] has proposed 'bluff' ads as an online click-fraud detection strategy to blacklist malicious publishers based on a predetermined threshold. The bluff ads are defined as sets of irrelevant ads that should never be clicked on and are displayed amongst a user's targeted ads. The underlying principle of [PERSON]'s model involves IP address monitoring, profile matching, threshold detection techniques and the use of bluff ads. One of the important objectives of this detection model is to make it more complicated for the botnet owners to train their software or a human operator on. The bluff ads contain irrelevant display text or highly relevant display text with irrelevant targeting information; thus, they work as a litmus test for the legitimacy of an individual's clicking on the ads. The bluff ads can also be seen as an approach to decrease the user's negative perceptions about user tracking by reducing the number of accurately targeted ads. It is worth mentioning that a'side effect' of running bluff ads could lead to an increase in advertising budgets for advertisers. Motivated by [PERSON]'s model, [PERSON] et al. [86] have proposed a methodology that enables advertisers to independently measure and compare click spam ratios on their ads by creating fake ads. They also have developed an automated method for ad networks to differentiate between various click spam attacks in a proactive manner. The primary idea of this approach is that the users associated with click-spam are actually not interested in the displayed ads; consequently, they are less likely to make any extra effort to reach the target website than a legitimately interested user. The advertisers can measure this difference and apply it to estimate the click-spam fraction. Nevertheless, the authors also have taken into account the possibility that some legitimately interested users might not make the extra effort, while some uninterested users do make the extra work to reach the target website. Moreover, the authors propose the use of Bayesian techniques to effectively cancel out quantities that cannot be directly measured so as to reduce the number of false positives and false negatives.