Identifying fraudulent requests for content

1. A method, comprising:

detecting user activity performed using a first device associated with a first user account;

generating a first user profile associated with the first user account, wherein the first user profile comprises at least one of:

activity information associated with the user activity;

demographic information associated with the first user account; or

interest information associated with the first user account;

receiving a request for content from a second device associated with the first user account;

determining, based upon the request for content, a resource associated with the request for content;

analyzing a user profile database based upon the first user profile to identify a set of user profiles similar to the first user profile and associated with one or more different users than the first user profile;

generating a relevance score corresponding to a probability that a first user with the first user profile accesses the resource and associated with the request for content, the generating the relevance score based upon (i) the resource determined based upon the request for content, (ii) the set of user profiles similar to the first user profile and associated with one or more different users than the first user profile and (iii) the first user profile associated with the first user account associated with the second device from which the request for content was received;

determining whether the request for content is fraudulent by comparing:

the relevance score corresponding to the probability that the first user with the first user profile accesses the resource, associated with the request for content and generated based upon (i) the resource determined based upon the request for content, (ii) the set of user profiles similar to the first user profile and associated with one or more different users than the first user profile and (iii) the first user profile associated with the first user account associated with the second device from which the request for content was received, with a threshold relevance; and

responsive to a determination that the request for content is fraudulent, at least one of:

not transmitting a content item, associated with the request for content, to the second device; or

discarding the request for content,

wherein at least one of:

the first user profile indicates one or more languages associated with the first user account: or

the generating the relevance score comprises:

determining, based upon the set of user profiles and the first user profile, an expected behavior associated with the first user account using one or more machine learning techniques; and

determining a difference between the expected behavior and a second behavior associated with the resource, wherein the relevance score is indicative of the difference.

2. The method of claim 1 , comprising:

receiving a second request for content from a third device; and

responsive to a determination that the second request for content is not fraudulent, transmitting a second content item, associated with the second request for content, to the third device.

3. The method of claim 2 , wherein the second content item is presented via the resource.

4. The method of claim 1 , wherein the resource is an internet resource.

5. The method of claim 4 , wherein the internet resource corresponds to a web page of a website.

6. The method of claim 4 , wherein the internet resource corresponds to an application.

7. The method of claim 4 , wherein the internet resource corresponds to a game.

8. The method of claim 1 , wherein the first device is the same as the second device.

9. The method of claim 1 , wherein the first device is different than the second device.

10. A computing device comprising:

a processor; and

memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:

receiving a request for content from a first device;

identifying a first user profile associated with the first device, wherein the first user profile comprises at least one of:

activity information associated with the first device;

demographic information associated with the first device; or

interest information associated with the first device;

determining, based upon the request for content, a resource associated with the request for content;

analyzing a user profile database based upon the first user profile to identify a set of user profiles similar to the first user profile;

generating a relevance score corresponding to a probability that a first user with the first user profile accesses the resource and associated with the request for content, the generating the relevance score based upon (i) the resource determined based upon the request for content, (ii) the set of user profiles similar to the first user profile and (iii) the first user profile associated with the first device from which the request for content was received;

determining whether the request for content is fraudulent by comparing:

the relevance score corresponding to the probability that the first user with the first user profile accesses the resource, associated with the request for content and generated based upon (i) the resource determined based upon the request for content, (ii) the set of user profiles similar to the first user profile and (iii) the first user profile associated with the first device from which the request for content was received, with a threshold relevance; and

responsive to a determination that the request for content is fraudulent, at least one of:

not transmitting a content item, associated with the request for content, to the first device; or

discarding the request for content,

wherein at least one of:

the first user profile indicates one or more languages associated with the first device; or

the generating the relevance score comprises:

determining, based upon the set of user profiles and the first user profile, an expected behavior associated with the first user profile using one or more machine learning techniques; and

determining a difference between the expected behavior and a second behavior associated with the resource, wherein the relevance score is indicative of the difference.

11. The computing device of claim 10 , the operations comprising:

receiving a second request for content from a second device; and

responsive to a determination that the second request for content is not fraudulent, transmitting a second content item, associated with the second request for content, to the second device.

12. The computing device of claim 11 , wherein the second content item is presented via the resource.

13. The computing device of claim 10 , wherein the resource is an internet resource corresponding to at least one of:

a web page of a website; or

an application.

14. The computing device of claim 10 , wherein the resource is an internet resource corresponding to a game.

15. A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:

receiving a request for content from a server associated with a resource, wherein the request for content is associated with a first device;

identifying a first user profile associated with the first device, wherein the first user profile comprises at least one of:

activity information associated with the first device;

demographic information associated with the first device; or

interest information associated with the first device;

analyzing a user profile database based upon the first user profile to identify a set of user profiles similar to the first user profile;

generating a relevance score corresponding to a probability that a first user with the first user profile accesses the resource and associated with the request for content, the generating the relevance score based upon (i) the resource associated with the request for content, (ii) the set of user profiles similar to the first user profile and (iii) the first user profile associated with the first device associated with the request for content;

determining whether the request for content is fraudulent by comparing:

the relevance score corresponding to the probability that the first user with the first user profile accesses the resource, associated with the request for content and generated based upon (i) the resource associated with the request for content, (ii) the set of user profiles similar to the first user profile and (iii) the first user profile associated with the first device associated with the request for content, with a threshold relevance; and

responsive to a determination that the request for content is fraudulent, at least one of:

not transmitting a content item, associated with the request for content, to the first device; or

discarding the request for content,

wherein at least one of:

the first user profile indicates one or more languages associated with the first device; or

the generating the relevance score comprises:

determining, based upon the set of user profiles and the first user profile, an expected behavior associated with the first user profile using one or more machine learning techniques; and

determining a difference between the expected behavior and a second behavior associated with the resource, wherein the relevance score is indicative of the difference.

16. The non-transitory machine readable medium of claim 15 , the operations comprising:

receiving a second request for content from a second device; and

responsive to a determination that the second request for content is not fraudulent, transmitting a second content item, associated with the second request for content, to the second device.

17. The non-transitory machine readable medium of claim 15 , wherein the first user profile indicates the one or more languages associated with the first device.

18. The non-transitory machine readable medium of claim 15 , wherein the generating the relevance score comprises:

determining the expected behavior; and

determining the difference between the expected behavior and the second behavior associated with the resource.

19. The non-transitory machine readable medium of claim 15 , the operations comprising:

responsive to the determination that the request for content is fraudulent, not transmitting the content item to the first device.

20. The non-transitory machine readable medium of claim 15 , the operations comprising:

responsive to the determination that the request for content is fraudulent, discarding the request for content.