Click fraud

Click fraud is a type of Internet crime that occurs in pay per click online advertising when a person, automated script or computer program imitates a legitimate user of a web browser clicking on an ad, for the purpose of generating a charge per click without having actual interest in the target of the ad's link. Click fraud is the subject of some controversy and increasing litigation due to the advertising networks being a key beneficiary of the fraud.

Use of a computer to commit this type of Internet fraud is a felony in many jurisdictions, for example Penal Code 502 in California, USA.^[1] There have been arrests relating to click fraud with regard to malicious clicking in order to deplete a competitor's advertising budget.^{[citation needed]}

Pay per click advertising

Main article: Pay per click

Pay per click advertising or, PPC advertising, is an arrangement in which webmasters (operators of Web sites), acting as publishers, display clickable links from advertisers in exchange for a charge per click. As this industry evolved, a number of advertising networks developed, which acted as middlemen between these two groups (publishers and advertisers). Each time a (believed to be) valid Web user clicks on an ad, the advertiser pays the advertising network, who in turn pays the publisher a share of this money. This revenue-sharing system is seen as an incentive for click fraud.

The largest of the advertising networks, Google's AdWords/AdSense and Yahoo! Search Marketing, act in a dual role, since they are also publishers themselves (on their search engines).^[2] According to critics, this complex relationship may create a conflict of interest. For instance, Google loses money to undetected click fraud when it pays out to the publisher, but it makes more money when it collects fees from the advertiser. Because of the spread between what Google collects and what Google pays out, click fraud directly and invisibly profits Google.^{[citation needed]}

Non-contracting parties

A secondary source of click fraud is non-contracting parties, who are not part of any pay-per-click agreement. This type of fraud is even harder to police, because perpetrators generally cannot be sued for breach of contract or charged criminally with fraud. Examples of non-contracting parties are:

• Competitors of advertisers: These parties may wish to harm a competitor who advertises in the same market by clicking on their ads. The perpetrators do not profit directly but force the advertiser to pay for irrelevant clicks, thus weakening or eliminating a source of competition.
• Competitors of publishers: These persons may wish to frame a publisher. It is made to look as if the publisher is clicking on its own ads. The advertising network may then terminate the relationship. Many publishers rely exclusively on revenue from advertising and could be put out of business by such an attack.
• Other malicious intent: As with vandalism, there is an array of motives for wishing to cause harm to either an advertiser or a publisher, even by people who have nothing to gain financially. Motives include political and personal vendettas. These cases are often the hardest to deal with, since it is difficult to track down the culprit, and if found, there is little legal action that can be taken against them.
• Friends of the publisher: Sometimes upon learning a publisher profits from ads being clicked, a supporter of the publisher (like a fan, family member, political party supporter, charity patron or personal friend) will click on the ads to help. This can be considered patronage. However, this can backfire when the publisher (not the friend) is accused of click fraud.

Advertising networks may try to stop fraud by all parties but often do not know which clicks are legitimate. Unlike fraud committed by the publisher, it is difficult to know who should pay when past click fraud is found. Publishers resent having to pay refunds for something that is not their fault. However, advertisers are adamant that they should not have to pay for phony clicks.

Organization

Click fraud can be as simple as one person starting a small Web site, becoming a publisher of ads, and clicking on those ads to generate revenue. Often the number of clicks and their value is so small that the fraud goes undetected. Publishers may claim that small amounts of such clicking is an accident, which is often the case.

Much larger-scale fraud also occurs.^[3] Those engaged in large-scale fraud will often run scripts which simulate a human clicking on ads in Web pages. However, huge numbers of clicks appearing to come from just one, or a small number of computers, or a single geographic area, look highly suspicious to the advertising network and advertisers. Clicks coming from a computer known to be that of a publisher also look suspicious to those watching for click fraud. A person attempting large-scale fraud, alone in their home, stands a good chance of being caught.

One type of fraud that circumvents detection based on IP patterns uses existing user traffic, turning this into clicks or impressions^[4] Such an attack can be camouflaged from users by using 0-size iframes to display advertisements that are programmatically retrieved using JavaScript. It could also be camouflaged from advertisers and portals by ensuring that so-called "reverse spiders" are presented with a legitimate page, while human visitors are presented with a page that commits click fraud. The use of 0-size iframes and other techniques involving human visitors may also be combined with the use of incentivized traffic, where members of "Paid to Read" sites are paid small amounts of money (often a fraction of a cent) to visit a website and/or click on keywords and search results, sometimes hundreds or thousands of times every day^[5] Some owners of PTR sites are members of PPC engines and may send many email ads to users who do search, while sending little ads to those who do not. They do this mainly because the charge per click on search results is often the only source of revenue to the site. This is known as forced searching, a practice that is frowned upon in the Get Paid To industry.

Organized crime can handle this by having many computers with their own Internet connections in different geographic locations. Often, scripts fail to mimic true human behavior, so organized crime networks use Trojan code to turn the average person's machines into zombie computers and use sporadic redirects or DNS cache poisoning to turn the oblivious user's actions into actions generating revenue for the scammer. It can be difficult for advertisers, advertising networks, and authorities to pursue cases against networks of people spread around multiple countries.

Impression fraud is when falsely generated ad impressions affect an advertiser's account. In the case of click-through rate based auction models, the advertiser may be penalized for having an unacceptably low click-through for a given keyword. This involves making numerous searches for a keyword without clicking of the ad. Such ads are disabled automatically, enabling a competitor's lower-bid ad for the same keyword to continue, while several high bidders (on the first page of the search results) have been eliminated.

Legal cases

Class action lawsuits

• Disputes over the issue have resulted in a number of lawsuits. In one case, Google (acting as both an advertiser and advertising network) won a lawsuit against a Texas company called Auction Experts (acting as a publisher), which Google accused of paying people to click on ads that appeared on Auction Experts' site, costing advertisers $50,000.^[6] Despite networks' efforts to stop it, publishers are suspicious of the motives of the advertising networks, because the advertising network receives money for each click, even if it is fraudulent.

• In July 2005, Yahoo settled a class-action lawsuit against it by plaintiffs alleging it did not do enough to prevent click fraud. Yahoo paid $4.5 million in legal bills for the plaintiffs and agreed to settle advertiser claims dating back to 2004^[7] In July 2006, Google settled a similar suit for $90 million.^[8][9]

• On March 8, 2006, Google agreed to a $90 million-settlement fund in the class-action lawsuit filed by Lane's Gifts & Collectibles.^[10] The class-action lawsuit was filed in Miller County, Arkansas, by Dallas attorneys Steve Malouf, Joel Fineberg, and Dean Gresham. The expert witness for the Plaintiffs in the case was Jessie Stricchiola, an internet search expert who first identified instances of ppc click fraud in 2001.^[11][12]

Michael Anthony Bradley

In 2004, California resident Michael Anthony Bradley created Google Clique, a software program that he claimed could let spammers defraud Google out of millions of dollars in fraudulent clicks.

Bradley used technology that he created for his other companies that took him five years to develop. Using this technology, he was able to demonstrate that fraud was possible, and was impossible for Google to detect.

Bradley notified Google of this security flaw, and was willing to work with them to close up some of these holes. However, Bradley was offered $500,000 for his software and technology by some of the world's top spammers. With this information, Bradley thought he could put a price of $100,000 on his technology, and offered to sell Google all rights to his technology, and they could make the Internet a better and safer place.

When Bradley showed up to Google's offices, he demoed the software for them, and when they asked what he wanted, he had stated that he would consult for free if they wanted to purchase the rights to his technology. He explained the prior offer of $500,000 and said he knew he could get it, but would settle for $100,000 if they wanted to work together.

Unknowingly, Bradley returned to Google's offices and was met by United States Secret Service officers who were undercover. They kept asking him what he wanted, and they even pushed a check for $100,000 to him, Bradley stated that this felt like blackmail and he was not comfortable with this, and pushed the money away. Just then the Secret Service came in and arrested him.

Authorities said he was arrested while trying to extort $100,000 from Google in exchange for handing over the program.^[13]

Charges were dropped without explanation on November 22, 2006; both the US Attorney's office and Google declined to comment. Business Week suggests that Google was unwilling to cooperate with the prosecution, as it would be forced to disclose its click fraud detection techniques publicly, as it also makes money from fraudulent clicks.^[14]

Solutions

Proving click fraud can be very difficult, since it is hard to know who is behind a computer and what their intentions are. 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,^[15] but none is foolproof.

The Tuzhilin Report^[16] produced 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 pay-per-click 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:

1. Forensic analysis of advertisers' web server log files.
   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.
2. Third-party corroboration.
   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.

Click fraud is less likely in cost per action models.

Research

The fact that the middlemen (search engines) have the upper hand in the operational definition of invalid clicks is the reason for the conflict of interest between advertisers and the middlemen, as described above. This is manifested in The Tuzhilin Report^[16] as described above. The Tuzhilin report did not publicly define invalid clicks and did not describe the operational definitions in detail. Rather, it gave a high-level picture of the fraud-detection system and argued that the operational definition of the search engine under investigations is "reasonable". One aim of the report was to preserve the privacy of the fraud-detection system in order to maintain its effectiveness. This prompted some researchers to conduct public research on how the middlemen can fight click fraud. Since such research is presumably not tainted by market forces, there is hope that this research can be adopted to assess how rigorous a middleman is in detecting click fraud in future law cases. The fear that this research can expose the internal fraud-detection system of middlemen still applies. An example of such research is that done by Metwally, Agrawal and El Abbadi at UCSB. Recent work by Majumdar, Kulkarni, and Ravishankar at UC Riverside proposes protocols for the identification of fraudulent behavior by brokers and other intermediaries in content-delivery networks.

See also

• Click farm
• Clickbot.A
• Hit inflation attack
• Web fraud detection

References

1. ^ California Penal Code §§ 484-502
2. ^ Asdemir, Kursad; Yurtseven, Özden; Yahya, Mon. An Economic Model of Click Fraud in Publisher Networks. 2008.
3. ^ Schonfeld, Erick; The Evolution Of Click Fraud: Massive Chinese Operation DormRing1 Uncovered". TechCrunch. October 8, 2009.
4. ^ Gandhi, Mona; Jakobsson, Markus; Ratkiewicz, Jacob;Badvertisements: Stealthy Click-Fraud with Unwitting Accessories", APWG eFraud conference, 2006.
5. ^ Grow, Bryan; Elgin, Ben; with Herbst, Moira; (October 2, 2006). "Click Fraud: The dark side of online advertising". BusinessWeek. http://www.businessweek.com/magazine/content/06_40/b4003001.htm. 
6. ^ Davis, Wendy; "Google Wins $75,000 in Click Fraud Case". Media Post July 5, 2005.
7. ^ Ryan, Kevin M. (July 5, 2006). "Big Yahoo Click Fraud Settlemen". iMedia Connection. http://www.imediaconnection.com/content/10294.asp. 
8. ^ Wong, Nicole; "Update Lanes Gifts v. Google". Google Blog, March 8, 2006
9. ^ Griffin, Joe E;"Lanes v. Google Final Order". Google Blog, July 27, 2006
10. ^ Sullivan, Danny;"Google Agrees To $90 Million Settlement In Class Action Lawsuit Over Click Fraud". March 8, 2006
11. ^ Stricchiola, Jessie (July 28, 2004). "Lost Per Click". Search Engine Watch. http://searchenginewatch.com/article/2065811/Lost-Per-Click-Search-Advertising-Click-Fraud. 
12. ^ {{cite web |last=Hartzer |first=Bill |url=http://www.searchengineguide.com/bill-hartzer/004579.html |title=Lost Clicks |publisher=Search Engine Guide |date= May 27, 2005{{
13. ^ US Department of Justice; "Computer Programmer Arrested for Extortion and Mail Fraud Scheme Targeting Google, Inc.". March 18, 2004
14. ^ Elgin, Ben; "The Vanishing Click Fraud Case". Business Week. December 4, 2006
15. ^ Ghosemajumder, Shuman; "Using data to help prevent fraud". March 18, 2008
16. ^ ^{a b} Tuzhilin, Alexander; The Lane's Gifts v. Google Report, by Alexander Tuzhilin. July, 2006

External links

• Metwally, Ahmed; Agrawal, Divyakant; El Abbadi, Amr (2007). "DETECTIVES: DETEcting Coalition hiT Inflation attacks in adVertising nEtworks Streams". Proceedings of the International WWW conference. IW3C2. pp. 241–250. http://www2007.org/papers/paper070.pdf. 
• Metwally, Ahmed; Agrawal, Divyakant; El Abbadi, Amr (2005). "Duplicate Detection in Click Streams". Proceedings of the International WWW conference. IW3C2. pp. 12–21. http://www2005.org/cdrom/docs/p12.pdf. 
• Majumdar, Saugat; Kulkarni, Dhananjay; Ravishankar, Chinya (2007). "Addressing Click Fraud in Content Delivery Systems". Infocom. IEEE. http://www.cs.ucr.edu/~ravi/Papers/NWConf/clickfraud.pdf. 
• "Truth in advertising", The Economist, November 23, 2006.
• "Vendors release click-fraud detection tools", eWeek. Retrieved March 4, 2005.
• "Click fraud roils search advertisers", CNet. Retrieved March 4, 2005.
• "Mice Attack: Internet scammers steal money with 'click fraud'", Newsweek. Retrieved January 18, 2005.
• "Google CFO: Fraud a Big Threat", CNN Money. Retrieved December 2, 2004.
• "How Click Fraud Could Swallow the Internet", Wired Magazine, issue 14.01 (January 2006). Retrieved December 29, 2005.
• "Click fraud fears growing for online advertisers", The Times. Retrieved February 2006.
• "New Attacks and Defenses In Click-Fraud War", Datamation. Retrieved September 2004.
• Simone Soubusta: "On Click Fraud", Information - Wissenschaft und Praxis. Retrieved March 2008.