Disclosed herein are cloud-based machine learning systems and methods for monitoring networked devices to identify and classify characteristics, to infer typical or atypical behavior and assign reputation profiles across various networked devices, and to make remediation recommendations. In some embodiments, a cloud-based machine learning system may learn the typical operation and interfacing of a plurality of reputable devices that are known to be free from malicious software and other threats. In some embodiments, a cloud-based machine learning system may learn the typical operation and interfacing of a device, and may identify atypical operations or interfaces associated with that device by comparing the operations and interfaces to those of a plurality of networked devices or to those of a defined standard reference device.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: identifying a collection of data, wherein the collection of data comprises device data generated by a plurality of networked devices; executing a machine learning algorithm on the collection of data to: aggregate received device data; assign a context to aggregated device data; generate aggregated device data characteristics; perform rules-based inference on identified characteristics; generate a ranking of inferred characteristics; and determine whether to make a remediation recommendation. 2. The method of claim 1, further comprising: executing a machine learning algorithm on the collection of data to further: make the remediation recommendation, if determined to make a remediation recommendation. 3. The method of claim 1, further comprising: executing a machine learning algorithm on the collection of data to further: receive feedback; and adapt rules-based inferences based on the feedback. 4. The method of claim 1, wherein determining whether to make a remediation recommendation is based on an identified characteristic in a device as compared to the identified characteristic in the plurality of networked devices. 5. The method of claim 1, further comprising: executing a machine learning algorithm on the collection of data to further: assign a reputation score based on the inferred device data characteristics. 6. The method of claim 1, wherein receiving the collection of data occurs intermittently for a period of time. 7. The method of claim 1, wherein receiving device data includes receiving device policy data; and wherein determining whether to make a remediation recommendation is based on the device policy data. 8. The method of claim 2, further comprising: executing a machine learning algorithm on the collection of data to further: receive a rejection of the remediation recommendation; and make an amended remediation recommendation. 9. An apparatus comprising: a memory element operable to store electronic code; and a processor operable to execute instructions associated with the electronic code, such that the apparatus is configured to: receive a collection of data, wherein the collection of data comprises device data generated by a plurality of networked devices; aggregate received device data; assign a context to aggregated device data; generate aggregated device data characteristics; perform rules-based inference on identified device data characteristics; generate a ranking for inferred device data characteristics; and determine whether to make a remediation recommendation. 10. The apparatus of claim 9, further configured to: make the remediation recommendation, if the processor determines to make a remediation recommendation. 11. The apparatus of claim 9, further configured to: receive feedback; and adapt rules-based inferences based on the feedback. 12. The apparatus of claim 9, further configured to: assign a reputation profile based on inferred device data characteristics. 13. The apparatus of claim 9, wherein determining whether to make a remediation recommendation is based on an identified characteristic in a device as compared to the identified characteristic in the plurality of networked devices. 14. The apparatus of claim 9, wherein receiving the collection of data occurs intermittently for a period of time. 15. The apparatus of claim 9, wherein receiving device data includes receiving device policy data; and wherein, determining whether to make a remediation recommendation, is based on the device policy data. 16. The apparatus of claim 10, further configured to: receive a rejection of the remediation recommendation; and make an amended remediation recommendation. 17. A system comprising: a plurality of networked devices; a device behavior model generator comprising: a memory element; and a processor, the processor operable to execute instructions to: receive a collection of data, wherein the collection of data comprises device data generated by a plurality of networked devices; and execute a machine learning algorithm on the collection of data to: aggregate received device data; assign a context to aggregated device data; generate aggregated device data characteristics; perform rules-based inference on identified device data characteristics; generate a ranking for inferred device data characteristics; and determine whether to make a remediation recommendation; and a network, wherein the network connects the plurality of networked devices and the device behavior model generator. 18. The system of claim 17, wherein the processor is further configured to execute a machine learning algorithm on the collection of data to: make the remediation recommendation, if the processor determines to make a remediation recommendation. 19. The system of claim 18, wherein the processor is further configured to execute a machine learning algorithm on the collection of data to: receive feedback and adapt rules-based inferences based on the feedback. 20. The system of claim 18, wherein the processor is further configured to execute a machine learning algorithm on the collection of data to: receive a rejection of the remediation recommendation; and make an amended remediation recommendation. 21. At least one machine accessible storage medium having instructions stored thereon, the instructions when executed on a machine, cause the machine to: receive a collection of data, wherein the collection of data comprises device data generated by a plurality of networked devices; execute a machine learning algorithm on the collection of data to: aggregate received device data; apply privacy policy to private device data; assign a context to aggregated device data; generate aggregated device data characteristics; perform rules-based inference on identified device data characteristics; generate a ranking for inferred device data characteristics; and determine whether to make a remediation recommendation. 22. The storage medium of claim 21, wherein the instructions, when executed, further cause a machine to: make the remediation recommendation, if the machine learning algorithm determines to make a remediation recommendation. 23. The storage medium of claim 21, wherein the instructions, when executed, further cause a machine to: receive feedback; and adapt rules-based inferences based on the feedback. 24. The storage medium of claim 21, wherein the instructions, when executed, further cause a machine to: assign a reputation profile based on inferred device data characteristics. 25. The storage medium of claim 21, wherein determining whether to make a remediation recommendation is based on an identified characteristic in a device as compared to the identified characteristic in the plurality of networked devices.