Device behavior analytics
A security platform architecture is described herein. A user identity platform architecture which uses a multitude of biometric analytics to create an identity token unique to an individual human. This token is derived on biometric factors like human behaviors, motion analytics, human physical characteristics like facial patterns, voice recognition prints, usage of device patterns, user location actions and other human behaviors which can derive a token or be used as a dynamic password identifying the unique individual with high calculated confidence. Because of the dynamic nature and the many different factors, this method is extremely difficult to spoof or hack by malicious actors or malware software.
1. A method programmed in a non-transitory memory of a device comprising:
monitoring behavior information of components of the device;
inputting the behavior information to a machine learning system;
generating a machine learning model;
comparing current behavior information with the machine learning model, wherein the machine learning model includes recorded device behavior information; and
affecting a device trust score based on the comparison of the current behavior information with the machine learning model, including decreasing the device trust score when the current behavior information is beyond a threshold related to the recorded device behavior information.
2. The method of claim 1 wherein monitoring the behavior information of components of the device includes reading, tracking and storing data related to the components of the device.
3. The method of claim 1 further comprising filtering the behavior information, including excluding a portion of the behavior information from the machine learning system.
4. The method of claim 1 wherein inputting the behavior information to the machine learning system includes training the machine learning system.
5. The method of claim 1 wherein affecting the device trust score based on the comparison of the current behavior information with the machine learning model includes decreasing the device trust score when the current behavior information is not sufficiently similar to the machine learning model.
6. The method of claim 5 further comprising limiting functionality of the device when the device trust score is below a threshold.
7. The method of claim 1 wherein the behavior information is from: a CPU, a GPU, a networking component, storage, a process/application, a sensor, a microphone/speaker, an audio/video/game processor/card/controller, a camera, a power source, a GPS device, a USB controller, an optical drive/device, and/or an input/output device.
8. A device comprising:
a non-transitory memory for storing an application, the application configured for:
monitoring behavior information of components of the device;
inputting the behavior information to a machine learning system;
generating a machine learning model;
comparing current behavior information with the machine learning model, wherein the machine learning model includes recorded device behavior information; and
affecting a device trust score based on the comparison of the current behavior information with the machine learning model, including decreasing the device trust score when the current behavior information is beyond a threshold related to the recorded device behavior information; and
a processor configured for processing the application.
9. The device of claim 8 wherein monitoring the behavior information of components of the device includes reading, tracking and storing data related to the components of the device.
10. The device of claim 8 further comprising filtering the behavior information, including excluding a portion of the behavior information from the machine learning system.
11. The device of claim 8 wherein inputting the behavior information to the machine learning system includes training the machine learning system.
12. The device of claim 8 wherein affecting the device trust score based on the comparison of the current behavior information with the machine learning model includes decreasing the device trust score when the current behavior information is not sufficiently similar to the machine learning model.
13. The device of claim 12 further comprising limiting functionality of the device when the device trust score is below a threshold.
14. The device of claim 8 wherein the behavior information is from: a CPU, a GPU, a networking component, storage, a process/application, a sensor, a microphone/speaker, an audio/video/game processor/card/controller, a camera, a power source, a GPS device, a USB controller, an optical drive/device, and/or an input/output device.
15. A method programmed in a non-transitory memory of a device comprising:
comparing current behavior information with a machine learning model, wherein the machine learning model includes recorded device behavior information; and
affecting a device trust score based on the comparison of the current behavior information with the machine learning model, including decreasing the device trust score when the current behavior information is beyond a threshold related to the recorded device behavior information.
16. The method of claim 15 further comprising monitoring the current behavior information of components of the device including reading, tracking and storing data related to the components of the device.
17. The method of claim 15 further comprising filtering behavior information, including excluding a portion of the behavior information from the machine learning model.
18. The method of claim 15 further comprising training a machine learning system to generate the machine learning model.
19. The method of claim 15 wherein affecting the device trust score based on the comparison of the current behavior information with the machine learning model includes decreasing the device trust score when the current behavior information is not sufficiently similar to the machine learning model.
20. The method of claim 19 further comprising limiting functionality of the device when the device trust score is below a threshold.
21. The method of claim 15 wherein the current behavior information is from:
a CPU, a GPU, a networking component, storage, a process/application, a sensor, a microphone/speaker, an audio/video/game processor/card/controller, a camera, a power source, a GPS device, a USB controller, an optical drive/device, and/or an input/output device.
22. A server device comprising:
a non-transitory memory for storing an application, the application configured for:
implementing a machine learning system;
receiving behavior information from a user device and inputting the behavior information into machine learning system;
generating a machine learning model using the machine learning system;
comparing current behavior information received from the user device with the machine learning model, wherein the machine learning model includes recorded device behavior information; and
affecting a trust score of the user device based on the comparison of the current behavior information with the machine learning model, including decreasing the device trust score when the current behavior information is beyond a threshold related to the recorded device behavior information; and
a processor configured for processing the application.
23. The server device of claim 22 wherein the behavior information of components of the user device is acquired by reading, tracking and storing data related to the components of the user device.
24. The server device of claim 22 further comprising filtering the behavior information, including excluding a portion of the behavior information from the machine learning system.
25. The server device of claim 22 wherein inputting the behavior information to the machine learning system includes training the machine learning system.
26. The server device of claim 22 wherein affecting the trust score of the user device based on the comparison of the current behavior information with the machine learning model includes decreasing the trust score of the user device when the current behavior information is not sufficiently similar to the machine learning model.
27. The server device of claim 26 further comprising limiting functionality of the user device when the trust score of the user device is below a threshold.
28. The server device of claim 22 wherein the behavior information is from: a CPU, a GPU, a networking component, storage, a process/application, a sensor, a microphone/speaker, an audio/video/game processor/card/controller, a camera, a power source, a GPS device, a USB controller, an optical drive/device, and/or an input/output device.