Threat detection within information systems
A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to receive a set of bad actor candidates that identifies one or more users from among the plurality of users. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a machine learning model. The machine learning model is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.
1. A network analysis device, comprising:
a memory operable to store network security rules, wherein the network security rules map network security threats to network security actions, wherein the network security actions comprise instructions for modifying device settings; and
a processor operably coupled to the memory, and configured to:
receive user activity information that comprises information about user interactions with a network device for a plurality of users, wherein at least a portion of the user activity information for the plurality of users comprises malicious or suspicious user activities;
determine a set of bad actor candidates from the plurality of users based on the user activity information for the plurality of users, wherein the set of bad actor candidates identifies one or more users from among the plurality of users that are associated with the malicious or suspicious user activities;
filter the user activity information based on the set of bad actor candidates, wherein filtering the user activity information comprises removing user activity information for users that are not members of the set of bad actor candidates;
input the filtered user activity information into a machine learning model, wherein the machine learning model is configured to:
receive the filtered user activity information; and
output system exposure information based on the user activity information, wherein the system exposure information identifies one or more network security threats;
identify one or more network security actions within the network security rules based on the one or more network security threats identified in the system exposure information; and
execute the one or more network security actions.
2. The device of claim 1 , wherein executing the one or more network security actions comprises:
identifying a website from within the filtered user activity information; and
identifying a user that visited the website from among the set of bad actor candidates;
restricting access to the website for the user.
3. The device of claim 1 , wherein executing the one or more network security actions comprises modifying settings for a user device that is associated with the user, wherein modifying the settings comprises restricting outgoing data for the user.
4. The device of claim 1 , wherein executing the one or more network security actions comprises modifying network settings for a user device that is associated with the user, wherein modifying the network settings comprises restricting access to a second network device.
5. The device of claim 1 , wherein executing the one or more network security actions comprises modifying permission settings for a user device that is associated with the user.
6. The device of claim 1 , wherein executing the one or more network security actions comprises restricting access to a physical location for a user.
7. The device of claim 1 , wherein executing the one or more network security actions comprises modifying settings for a user device that is associated with the user, wherein modifying the settings comprises restricting access to an application for the user.
8. A threat detection method, comprising:
receiving user activity information that comprises information about user interactions with a network device for a plurality of users, wherein at least a portion of the user activity information for the plurality of users comprises malicious or suspicious user activities;
determining a set of bad actor candidates from the plurality of users based on the user activity information for the plurality of users, wherein the set of bad actor candidates identifies one or more users from among the plurality of users that are associated with the malicious or suspicious user activities;
filtering the user activity information based on the set of bad actor candidates, wherein filtering the user activity information comprises removing user activity information for users that are not members of the set of bad actor candidates;
inputting the filtered user activity information into a machine learning model, wherein the machine learning model is configured to:
receive the filtered user activity information; and
output system exposure information based on the user activity information, wherein the system exposure information identifies one or more network security threats;
identifying one or more network security actions based on the one or more network security threats identified in the system exposure information, wherein the one or more network security actions comprise instructions for modifying device settings; and
executing the one or more network security actions.
9. The method of claim 8 , wherein executing the one or more network security actions comprises:
identifying a website from within the filtered user activity information;
identifying a user that visited the website from among the set of bad actor candidates; and
restricting access to the website for the user.
10. The method of claim 8 , wherein executing the one or more network security actions comprises modifying settings for a user device that is associated with the user, wherein modifying the settings comprises restricting outgoing data for the user.
11. The method of claim 8 , wherein executing the one or more network security actions comprises modifying network settings for a user device that is associated with the user, wherein modifying the network settings comprises restricting access to a second network device.
12. The method of claim 8 , wherein executing the one or more network security actions comprises modifying permission settings for a user device that is associated with the user.
13. The method of claim 8 , wherein executing the one or more network security actions comprises restricting access to a physical location for a user.
14. The method of claim 8 , wherein executing the one or more network security actions comprises modifying settings for a user device that is associated with the user, wherein modifying the settings comprises restricting access to an application for the user.
15. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to:
receive user activity information that comprises information about user interactions with a network device for a plurality of users, wherein at least a portion of the user activity information for the plurality of users comprises malicious or suspicious user activities;
determine a set of bad actor candidates from the plurality of users based on the user activity information for the plurality of users, wherein the set of bad actor candidates identifies one or more users from among the plurality of users that are associated with the malicious or suspicious user activities;
filter the user activity information based on the set of bad actor candidates, wherein filtering the user activity information comprises removing user activity information for users that are not members of the set of bad actor candidates;
input the filtered user activity information into a machine learning model, wherein the machine learning model is configured to:
receive the filtered user activity information; and
output system exposure information based on the user activity information, wherein the system exposure information identifies one or more network security threats;
identify one or more network security actions based on the one or more network security threats identified in the system exposure information, wherein the one or more network security actions comprise instructions for modifying device settings; and
execute the one or more network security actions.
16. The non-transitory computer-readable medium of claim 15 , wherein executing the one or more network security actions comprises:
identifying a website from within the filtered user activity information;
identifying a user that visited the website from among the set of bad actor candidates; and
restricting access to the website for the user.
17. The non-transitory computer-readable medium of claim 15 , wherein executing the one or more network security actions comprises modifying settings for a user device that is associated with the user, wherein modifying the settings comprises restricting outgoing data for the user.
18. The non-transitory computer-readable medium of claim 15 , wherein executing the one or more network security actions comprises modifying network settings for a user device that is associated with the user, wherein modifying the network settings comprises restricting access to a second network device.
19. The non-transitory computer-readable medium of claim 15 , wherein executing the one or more network security actions comprises restricting access to a physical location for a user.
20. The non-transitory computer-readable medium of claim 15 , wherein executing the one or more network security actions comprises modifying settings for a user device that is associated with the user, wherein modifying the settings comprises restricting access to an application for the user.