Click fraud detection

• US 8,682,718 B2
• Filed: 12/14/2011
• Issued: 03/25/2014
• Est. Priority Date: 09/19/2006
• Status: Active Grant

• Current Assignee: Gula Consulting Limited Liability Company (Intellectual Ventures LLC)
• Original Assignee: GERE DEV Applications LLC (Intellectual Ventures LLC)
• Inventors: Zwicky, Richard Kazimierz
• Primary Examiner(s): ROBINSON, GRETA LEE
• Application Number:
  US13/325,600
• Publication Number:
  US 20120084146A1
• Time in Patent Office: 832 Days
• Field of Search: 707/687, 707/688, 707/690, 707/705, 707/999.107, 705/50, 705/52, 705/64, 705/65, 705/72, 705/75, 705/14.15, 705/14.26, 705/317, 705/318, 705/7.28, 705/7.29, 705/14.4, 709/224
• US Class Current: 705/14.26
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0225 Avoiding frauds
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: G06Q 30/0248 Avoiding fraud

Abstract

Systems and methods for detecting instances of click fraud are disclosed. Click fraud occurs when, for example, a user, malware, bot, or the like, clicks on a pay per click advertisement (e.g., hyperlink), a paid search listing, or the like without a good faith interest in the underlying subject of the hyperlink. Such fraudulent clicks can be expensive for an advertising sponsor. Statistical information, such as ratios of unpaid clicks to pay per clicks, are extracted from an event database. The statistical information of global data is used as a reference data set to compare to similar statistical information for a local data set under analysis. In one embodiment, when the statistical data sets match relatively well, no click fraud is determined to have occurred, and when the statistical data sets do not match relatively well, click fraud is determined to have occurred.

Claims

1. A computer-implemented method for detecting click fraud, the method comprising:

determining a subset of activity data from a stored set of activity data, the set of activity data comprising data indicative of visits to one or more websites during a period of time, and the subset of activity data comprising data indicative of visits to the one or more websites during a portion of the period of time;

comparing statistical information about the set of activity data to statistical information about the subset of activity data, wherein the statistical information about the set of activity data comprises a comparison of unpaid referrals in the set of activity data to paid referrals in the set of activity data, and wherein the statistical information about the subset of activity data comprises a comparison of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data; and

assessing whether click fraud is present based at least in part on the comparison of the statistical information about the set of activity data to the statistical information about the subset of activity data.

2. The method of claim 1, further comprising:

generating the statistical information about the set of activity data, wherein the generating comprises calculating one or more ratios of unpaid referrals in the set of activity data to paid referrals in the set of activity data.

3. The method of claim 2, wherein generating the statistical information about the set of activity data further comprises:

calculating at least one of standard deviation, skewness, and kurtosis of the calculated one or more ratios of unpaid referrals in the set of activity data to paid referrals in the set of activity data.

4. The method of claim 1, further comprising:

generating the statistical information about the subset of activity data, wherein the generating comprises calculating one or more ratios of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data.

5. The method of claim 4, wherein generating the statistical information about the subset of activity data further comprises:

calculating at least one of standard deviation, skewness, and kurtosis of the calculated one or more ratios of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data.

6. The method of claim 1, wherein the set of activity corresponds to about a week of data, and wherein the subset of activity corresponds to about an hour of data.

7. The method of claim 1, wherein the paid referrals correspond to pay per click referrals.

8. The method of claim 1, wherein the paid referrals correspond to pay per impression referrals.

9. A system for detecting click fraud, comprising:

one or more memories configured to store a set of activity data; and

one or more computing devices configured to;

determine a subset of activity data from the set of activity data, the set of activity data comprising data indicative of visits to one or more websites during a period of time, and the subset of activity data comprising data indicative of visits to the one or more websites during a portion of the period of time,

compare statistical information about the set of activity data to statistical information about the subset of activity data, wherein the statistical information about the set of activity data comprises a comparison of unpaid referrals in the set of activity data to paid referrals in the set of activity data, and wherein the statistical information about the subset of activity data comprises a comparison of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data, and

assess whether click fraud is present based at least in part on the comparison of the statistical information about the set of activity data to the statistical information about the subset of activity data.

10. The system of claim 9, wherein the one or more computing devices are further configured to:

generate the statistical information about the set of activity data by calculating one or more ratios of unpaid referrals in the set of activity data to paid referrals in the set of activity data.

11. The system of claim 10, wherein the one or more computing devices are further configured to:

calculate at least one of standard deviation, skewness, and kurtosis of the calculated one or more ratios of unpaid referrals in the set of activity data to paid referrals in the set of activity data.

12. The system of claim 9, wherein the one or more computing devices are further configured to:

generate the statistical information about the subset of activity data by calculating one or more ratios of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data.

13. The system of claim 12, wherein the one or more computing devices are further configured to:

calculate at least one of standard deviation, skewness, and kurtosis of the calculated one or more ratios of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data.

14. A computer readable medium having instructions stored thereon for detecting click fraud, the instructions comprising:

instructions to determine a subset of activity data from a stored set of activity data, the set of activity data comprising data indicative of visits to one or more websites during a period of time, and the subset of activity data comprising data indicative of visits to the one or more websites during a portion of the period of time;

instructions to compare statistical information about the set of activity data to statistical information about the subset of activity data, wherein the statistical information about the set of activity data comprises a comparison of unpaid referrals in the set of activity data to paid referrals in the set of activity data, and wherein the statistical information about the subset of activity data comprises a comparison of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data; and

instructions to assess whether click fraud is present based at least in part on the comparison of the statistical information about the set of activity data to the statistical information about the subset of activity data.

15. The computer readable medium of claim 14, the instructions further comprising:

instructions to generate the statistical information about the set of activity data, wherein the generating comprises calculating one or more ratios of unpaid referrals in the set of activity data to paid referrals in the set of activity data.

16. The computer readable medium of claim 15, wherein the instructions to generate the statistical information about the set of activity data comprise:

instructions to calculate at least one of standard deviation, skewness, and kurtosis of the calculated one or more ratios of unpaid referrals in the set of activity data to paid referrals in the set of activity data.

17. The computer readable medium of claim 14, the instructions further comprising:

instructions to generating the statistical information about the subset of activity data, wherein the generating comprises calculating one or more ratios of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data.

18. The computer readable medium of claim 17, wherein the instructions to generate the statistical information about the subset of activity data comprise:

instructions to calculate at least one of standard deviation, skewness, and kurtosis of the calculated one or more ratios of unpaid referrals in the subset of activity data to paid referrals in the subset of activity data.

19. The computer readable medium of claim 14, wherein the paid referrals correspond to pay per click referrals.

20. The computer readable medium of claim 14, wherein the paid referrals correspond to pay per impression referrals.