Editing Click fraud (section)

==Solutions==
Proving click fraud can be very difficult since it is hard to know who is behind a computer and what their intentions are. When it comes to mobile ad fraud detection, data analysis can give some reliable indications. Abnormal metrics can hint at the presence of different types of frauds. To detect click fraud in the ad campaign, advertisers can focus on the following attribution points:<ref name="mfaas">[https://mfaas.com/resources/click-fraud-prevention/ "Click Fraud Prevention – Identify & Reduce Bot Traffic in Your Paid Ads"]. June 7, 2019</ref>

* '''IP Address''': As bots run similar scripts from the same server, any click fraud on mobile ads will indicate a high density of clicks coming from the same IP address or a range of similar IP addresses. Advertisers can also run check on IP addresses to verify their history with another fraud.
* '''Click Timestamp''': Click timestamp maintains the time at which the click is made on the ad. The bot-based click fraud runs repeatedly to attempt clicking on the ads, which increases click frequency for that duration. A high range of clicks with almost similar timestamp points at the possibility of click fraud. A low duration and high frequency mean a high probability of fraud.
* '''Action Timestamp''': Action timestamp is the time at which the user takes action on (or engages with) the app or website. With a bot-based click attack, there can be a similarity with action timestamp. As bot clicks on the advertisement and then performs the action on app or website without any delay, the advertiser can notice a low or almost no action timestamp.

Often the best an advertising network can do is to identify which clicks are most likely fraudulent and not charge the account of the advertiser. Even more sophisticated means of detection are used,<ref name="google">[[Shuman Ghosemajumder|Ghosemajumder, Shuman]]; [http://googleblog.blogspot.com/2008/03/using-data-to-help-prevent-fraud.html "Using data to help prevent fraud"]. March 18, 2008</ref> but none are foolproof.

The Tuzhilin Report<ref name="Tuzhilin">Tuzhilin, Alexander; [http://googleblog.blogspot.com/pdf/Tuzhilin_Report.pdf The Lane's Gifts v. Google Report], by [[Alexander Tuzhilin]]. July, 2006</ref> produced by [[Alexander Tuzhilin]] as part of a click fraud lawsuit settlement, has a detailed and comprehensive discussion of these issues.  In particular, it defines "the Fundamental Problem of invalid (fraudulent) clicks":
* "There is no conceptual definition of invalid clicks that can be operationalized [except for certain obviously clear cases]."
* "An operational definition cannot be fully disclosed to the general public because of the concerns that unethical users will take advantage of it, which may lead to a massive click fraud. However, if it is not disclosed, advertisers cannot verify or even dispute why they have been charged for certain clicks."

The PPC industry is lobbying for tighter laws on the issue. Many hope to have laws that will cover those not bound by contracts.

A number of companies are developing viable solutions for click fraud identification and are developing intermediary relationships with advertising networks. Such solutions fall into two categories:

# ''Forensic analysis of advertisers' web server log files.''<br>This analysis of the advertiser's web server data requires an in-depth look at the source and behavior of the traffic. As industry standard log files are used for the analysis, the data is verifiable by advertising networks. The problem with this approach is that it relies on the honesty of the middlemen in identifying fraud.
# ''Third-party corroboration.''<br>Third parties offer web-based solutions that might involve placement of single-pixel images or Javascript on the advertiser's web pages and suitable tagging of the ads. The visitor may be presented with a cookie. Visitor information is then collected in a third-party data store and made available for download. The better offerings make it easy to highlight suspicious clicks, and they show the reasons for such a conclusion. Since an advertiser's log files can be tampered with, their accompaniment with corroborating data from a third-party forms a more convincing body of evidence to present to the advertising network. However, the problem with third-party solutions is that such solutions see only part of the traffic of the entire network. Hence, they can be less likely to identify patterns that span several advertisers. In addition, due to the limited amount of traffic they receive when compared to middlemen, they can be overly or less aggressive when judging traffic to be fraud.

In a 2007 interview in [[Forbes]], Google click fraud prevention expert [[Shuman Ghosemajumder]] said that one of the key challenges in click fraud detection by third-parties was access to data beyond clicks, notably, ad impression data.<ref name="forbes">Greenberg, Andy; [https://www.forbes.com/2007/09/13/google-shuman-fraud-tech-cx_ag_0914google.html "Counting Clicks"]. [[Forbes]]. September 14, 2007</ref>

Click fraud is less likely in [[cost per action]] models.