## 8 Taxonomy of Ad Fraud Prevention and Detection Methods
### Prevention of Digital Advertising Fraud  
[PERSON] has said: \"Prevention is better than cure\". To effectively prevent fraud in a digital advertising environment, a comprehensive across-the-board endeavor is required to force all parties to follow proper advertising strategies and increase public awareness of the unethical motivators that bring fraud into the ad ecosystem. One of the preliminary steps in avoiding fraud in ad-serving platforms is to study ad banner viewability and arrange web pages in a way that is easy to identify potential placement vulnerabilities fraudsters seek after. Additionally, Google AdSense [94] suggests placing ads closer to the contents of the host web page; however, ads should be completely distinguishable from the main web page contents so as not to misguide users. It is also mentioned in [95] that putting many ad placements on one single page increases the possibility of stuffing and stacking fraud. Google AdMob [95] recommends not placing ads in areas that can be easily clicked on by accident. A prevention procedure proposed by [PERSON] et al. [96] aims to avoid click inflation fraud conducted by the publisher. The study expresses the importance of verifying the publishers' identification before making any decision (such as signing them up in the ad network). The AdSense-like syndicate ads program may already reject registering a publisher due to its invalid identification. It should be stressed that nefarious publishers constantly attempt to sign up with invalid, fraudulent names to hide their identities and mislead the other parties in the ad system. [PERSON] et al. [97] suggest that advertisers should focus more on users' actions than just a raw number of impressions provided by publishers. In fact, advertisers should go beyond the viewability and demand a full transparency about the source of each ad impression. The researchers also advocate for a novel reverse proxy approach innovated by Distil Networks, which inspects each HTTP request in real-time and determines if it is generated by a bot or not. ANA/WhiteOps [98] encourages all parties in the ad ecosystem to combat fraud by frequently updating and deploying IP blacklisting so as to prevent displaying of ads to those IPs. Namely, entities behind blacklisted IPs would likely produce fraudulent impressions, clicks or other more involved actions. In addition to the above straightforward recommendation, the more comprehensive solutions for the prevention of fraud in ad platforms can be classified into the following four categories [1; 99]: honeypot-based, signature-based, anomaly-based, and credential-based prevention. * Honeypot-based prevention approach (i.e., bluff ads) is a mechanism that allows advertisers to serve some small, unrecognizable bluff or honeypot ads in order to detect fraudulent activities in the ad system. The different conversion rates between the bluff-ads and the legitimate-ads can be used as an indicator to detect ongoing fraudulent activities. * Signature-based method refers to identifying and preventing malicious traffic and bogus impressions by hunting specific patterns or features in the traffic. This mechanism uses a predefined pattern (signature) to decide if traffic is valid or not. For example, a typical signature is click count on published ads in order to detect duplicate clicks. * Anomaly-based prevention technique applies statistical analysis and historical data to identify patterns of fraudulent behavior, and then use those patterns in order to detect suspicious ad placements and/or abnormal traffic. * Credential-based prevention mechanism (also known as Website Popularity or Page Ranking) refers to the task of assessing the creditability of a publisher or an advertiser in order to discover the authenticity of their web page contents or the number of impressions they generate. The reverse crawling (reverse engineering is the process of understanding the functioning and structure of a website and its information [100].