Systems and methods for analyzing network data to identify human and non-human users in network communications

1. A computer-implemented method for identifying human users on a network, comprising:

receiving network data comprising data transmitted over a network over a predetermined time period, the network data comprising a plurality of user-initiated events and a plurality of network addresses, wherein each of the plurality of network addresses is associated with at least one of the plurality of user-initiated events;

determining a plurality of pairs, each pair of the plurality of pairs comprising a user-initiated event of the plurality of user-initiated events and an associated network address of the plurality of network addresses;

determining one or more event frequency thresholds, the frequency thresholds based on the user-initiated events and the plurality of network addresses providing an indicia that a user is human;

determining one or more consistency thresholds based on success and failure rates of the user-initiated events and the plurality of network addresses;

determining qualifying pairs of the plurality of pairs, the qualifying pairs corresponding to a subset of the plurality of pairs that meet or exceed one or more event frequency thresholds or one or more consistency thresholds;

determining non-qualifying pairs of the plurality of pairs, the non-qualifying pairs corresponding to the subset of the plurality of pairs that do not meet or exceed one or more event frequency thresholds or one or more consistency thresholds;

generating at least a first distribution associated with the qualifying pairs and a second distribution associated with the non-qualifying pairs;

based on at least the first distribution and the second distribution, determining if at least one user-initiated event of the plurality of user-initiated events is associated with a human user or a non-human user; and

based on an indicia that at least one user-initiated event of the plurality of user-initiated events is associated with a non-human user, but should be associated with a human user, adjusting the event frequency threshold.

2. The method of claim 1 , wherein the plurality of network addresses comprises a number of successful login attempts and/or a number of failed login attempts associated with each user-initiated event of the plurality of user-initiated events, and further comprising:

determine if the at least one user-initiated event is associated with a human user or a non-human user based upon the number of successful login attempts and the number of failed login attempts for each of the plurality of user-initiated events.

3. The method of claim 1 , further comprising:

determining an indicia that a user is human is based on a user's address book.

4. The method of claim 1 , further comprising:

receiving additional network data associated with a plurality of known human users; and

determining at least one of the event frequency thresholds based, at least in part, on the plurality of known human users.

5. The method of claim 1 , further comprising:

associating, for the plurality of user-initiated events, a plurality of booleans with the one or more event frequency thresholds;

determining, for each of the plurality of user-initiated events, whether at least one of the plurality of booleans is true or false based upon whether any of the one or more event frequency thresholds are met or exceeded; and

determining if each of the plurality of user-initiated events is associated with a human user or a non-human user based upon whether each of the plurality of booleans is true or false and based upon at least the first and second distributions.

6. The method of claim 5 , further comprising:

determining at least one uncertain user-initiated event of the plurality of user-initiated events that neither meets criteria for being associated with a human user or a non-human user;

adjusting the one or more event frequency thresholds; and

determining if the at least one uncertain user-initiated event is associated with a human user or a non-human user based upon the adjusted event frequency thresholds.

7. The method of claim 1 , further comprising:

generating pairs of user-initiated events from the plurality of user-initiated events;

determining communications between user-initiated events associated with each of the pairs of user-initiated events; and

labeling the plurality of user-initiated events as trusted or untrusted based upon the communications between user-initiated events associated with each of the pairs of user-initiated events.

8. A system for identifying human users on a network, the system including:

a data storage device that stores instructions for identifying human users on a network; and

a processor configured to execute the instructions to perform a method including:

receiving network data comprising data transmitted over a network over a predetermined time period, the network data comprising a plurality of user-initiated events and a plurality of network addresses, wherein each of the plurality of network addresses is associated with at least one of the plurality of user-initiated events;

determining a plurality of pairs, each pair of the plurality of pairs comprising a user-initiated event of the plurality of user-initiated events and an associated network address of the plurality of network addresses;

determining one or more event frequency thresholds, the frequency thresholds based on the user-initiated events and the plurality of network addresses providing an indicia that a user is human;

determining one or more consistency thresholds based on success and failure rates of the user-initiated events and the plurality of network addresses;

determining qualifying pairs of the plurality of pairs, the qualifying pairs corresponding to a subset of the plurality of pairs that meet or exceed one or more event frequency thresholds or one or more consistency thresholds;

determining non-qualifying pairs of the plurality of pairs, the non-qualifying pairs corresponding to the subset of the plurality of pairs that do not meet or exceed one or more event frequency thresholds or one or more consistency thresholds;

generating at least a first distribution associated with the qualifying pairs and a second distribution associated with the non-qualifying pairs;

based on at least the first distribution and the second distribution, determining if at least one user-initiated event of the plurality of user-initiated events is associated with a human user or a non-human user; and

based on an indicia that at least one user-initiated event of the plurality of user-initiated events is associated with a non-human user, but should be associated with a human user, adjusting the event frequency threshold.

9. The system of claim 8 , wherein the plurality of network addresses comprises a number of successful login attempts and/or a number of failed login attempts associated with each user-initiated event of the plurality of user-initiated events, and the method further comprising:

determine if the at least one user-initiated event is associated with a human user or a non-human user based upon the number of successful login attempts and the number of failed login attempts for each of the plurality of user-initiated events.

10. The system of claim 8 , the method further comprising:

determining an indicia that a user is human is at based on a user's address book.

11. The system of claim 8 , the method further comprising:

receiving additional network data associated with a plurality of known human users; and

determining at least one of the event frequency thresholds based, at least in part, on the plurality of known human users.

12. The system of claim 8 , the method further comprising:

associating, for the plurality of user-initiated events, a plurality of booleans with the one or more event frequency thresholds;

determining, for each of the plurality of user-initiated events, whether at least one of the plurality of booleans is true or false based upon whether any of the one or more event frequency thresholds are met or exceeded; and

determining if each of the plurality of user-initiated events is associated with a human user or a non-human user based upon whether each of the plurality of booleans is true or false and based upon at least the first and second distributions.

13. The system of claim 12 , the method further comprising:

determining at least one uncertain user-initiated event of the plurality of user-initiated events that neither meets criteria for being associated with a human user or a non-human user;

adjusting the one or more event frequency thresholds; and

determining if the at least one uncertain user-initiated event is associated with a human user or a non-human user based upon the adjusted event frequency thresholds.

14. The system of claim 8 , the method further comprising:

generating pairs of user-initiated events from the plurality of user-initiated events;

determining communications between user-initiated events associated with each of the pairs of user-initiated events; and

labeling the plurality of user-initiated events as trusted or untrusted based upon the communications between user-initiated events associated with each of the pairs of user-initiated events.

15. A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method for identifying human users on a network, the method including:

receiving network data comprising data transmitted over a network over a predetermined time period, the network data comprising a plurality of user-initiated events and a plurality of network addresses, wherein each of the plurality of network addresses is associated with at least one of the plurality of user-initiated events;

determining a plurality of pairs, each pair of the plurality of pairs comprising a user-initiated event of the plurality of user-initiated events and an associated network address of the plurality of network addresses;

determining one or more event frequency thresholds, the frequency thresholds based on the user-initiated events and the plurality of network addresses providing an indicia that a user is human;

determining one or more consistency thresholds based on success and failure rates of the user-initiated events and the plurality of network addresses;

determining qualifying pairs of the plurality of pairs, the qualifying pairs corresponding to a subset of the plurality of pairs that meet or exceed one or more event frequency thresholds or one or more consistency thresholds;

determining non-qualifying pairs of the plurality of pairs, the non-qualifying pairs corresponding to the subset of the plurality of pairs that do not meet or exceed one or more event frequency thresholds or one or more consistency thresholds;

generating at least a first distribution associated with the qualifying pairs and a second distribution associated with the non-qualifying pairs;

based on at least the first distribution and the second distribution, determining if at least one user-initiated event of the plurality of user-initiated events is associated with a human user or a non-human user; and

based on an indicia that at least one user-initiated event of the plurality of user-initiated events is associated with a non-human user, but should be associated with a human user, adjusting the event frequency threshold.

16. The computer-readable medium of claim 15 , wherein the plurality of network addresses comprises a number of successful login attempts and/or a number of failed login attempts associated with each user-initiated event of the plurality of user-initiated events, and the method further comprising:

determine if the at least one user-initiated event is associated with a human user or a non-human user based upon the number of successful login attempts and the number of failed login attempts for each of the plurality of user-initiated events.

17. The computer-readable medium of claim 15 , the method further comprising:

receiving additional network data associated with a plurality of known human users; and

determining at least one of the event frequency thresholds based, at least in part, on the plurality of known human users.

18. The computer-readable medium of claim 15 , the method further comprising:

associating, for the plurality of user-initiated events, a plurality of booleans with the one or more event frequency thresholds;

determining, for each of the plurality of user-initiated events, whether at least one of the plurality of booleans is true or false based upon whether any of the one or more event frequency thresholds are met or exceeded; and

determining if each of the plurality of user-initiated events is associated with a human user or a non-human user based upon whether each of the plurality of booleans is true or false and based upon at least the first and second distributions.

19. The computer-readable medium of claim 18 , the method further comprising:

determining at least one uncertain user-initiated event of the plurality of user-initiated events that neither meets criteria for being associated with a human user or a non-human user;

adjusting the one or more event frequency thresholds; and

determining if the at least one uncertain user-initiated event is associated with a human user or a non-human user based upon the adjusted event frequency thresholds.

20. The computer-readable medium of claim 15 , the method further comprising:

generating pairs of user-initiated events from the plurality of user-initiated events;

determining communications between user-initiated events associated with each of the pairs of user-initiated events; and

labeling the plurality of user-initiated events as trusted or untrusted based upon the communications between user-initiated events associated with each of the pairs of user-initiated events.