Learning and assessing device classification rules

1. A method comprising:

obtaining, by a device, a set of device classification rules, each device classification rule specifying one or more attributes from a set of attributes and being configured to assign a device type to an endpoint in a network when the endpoint exhibits the one or more attributes specified by that rule;

forming, by the device, a graphical representation of the set of attributes by generating a concept lattice based on the set of attributes that represents a plurality of potential device classification rules, wherein the graphical representation of the set of attributes comprises a plurality of nodes and a plurality of edges interconnecting the plurality of nodes, each node representing a particular subset of the set of attributes being exhibited by one or more endpoints in the network, and each edge representing a relationship between two nodes;

performing, by the device, an analysis of the graphical representation of the set of attributes;

selecting, by the device and based on the analysis of the graphical representation of the set of attributes, a particular device classification rule from among the plurality of potential device classification rules configured to assign a device type to the one or more endpoints; and

providing, by the device, a result of the analysis to a user interface.

2. The method as in claim 1 , wherein the device type is indicative of one or more of: a manufacturer of the endpoint, a model of the endpoint, or a software version associated with the endpoint.

3. The method as in claim 1 , wherein performing the analysis of the graphical representation of the set of attributes:

computing relevancy scores for the plurality of potential device classification rules based on their similarities to each of the set of device classification rules; and

selecting the particular device classification rule from among the plurality of potential device classification rules based on its relevancy score.

4. The method as in claim 3 , wherein providing the result of the analysis to the user interface comprises:

providing the particular device classification rule to the user interface; and

receiving feedback regarding the particular device classification rule from the user interface.

5. The method as in claim 1 , wherein forming the graphical representation of the set of attributes comprises:

generating a master tree representation of the set of device classification rules.

6. The method as in claim 5 , wherein performing the analysis of the graphical representation of the set of attributes comprises:

using the master tree representation to determine metrics comprising at least one of: measures of conflict among the set of device classification rules, measures of stability of classifications by the device classification rules, or measures of time needed to apply the device classification rules to endpoint devices.

7. The method as in claim 6 , further comprising:

optimizing at least one of the set of device classification rules based on the metrics.

8. The method as in claim 5 , wherein the set of device classification rules are obtained from a plurality of source systems, and wherein providing the result of the analysis to the user interface comprises:

providing a comparison of the plurality of source systems to the user interface.

9. An apparatus, comprising:

one or more network interfaces;

a processor coupled to the one or more network interfaces and configured to execute one or more processes; and

a memory configured to store a process that is executable by the processor, the process when executed configured to:

obtain a set of device classification rules, each device classification rule specifying one or more attributes from a set of attributes and being configured to assign a device type to an endpoint in a network when the endpoint exhibits the one or more attributes specified by that rule;

form a graphical representation of the set of attributes by generating a concept lattice based on the set of attributes that represents a plurality of potential device classification rules, wherein the graphical representation of the set of attributes comprises a plurality of nodes and a plurality of edges interconnecting the plurality of nodes, each node representing a particular subset of the set of attributes being exhibited by one or more endpoints in the network, and each edge representing a relationship between two nodes;

perform an analysis of the graphical representation of the set of attributes;

select, based on the analysis of the graphical representation of the set of attributes, a particular device classification rule from among theft plurality of potential device classification rules configured to assign a device type to the one or more endpoints; and

provide a result of the analysis to a user interface.

10. The apparatus as in claim 9 , wherein the device type is indicative of one or more of: a manufacturer of the endpoint, a model of the endpoint, or a software version associated with the endpoint.

11. The apparatus as in claim 10 , wherein the apparatus performs the analysis of the graphical representation of the set of attributes by:

computing relevancy scores for the plurality of potential device classification rules based on their similarities to each of the set of device classification rules; and

selecting the particular device classification rule from among the plurality of potential device classification rules based on its relevancy score.

12. The apparatus as in claim 11 , wherein the apparatus provides the result of the analysis to the user interface by:

providing the particular device classification rule to the user interface; and

receiving feedback regarding the particular device classification rule from the user interface.

13. The apparatus as in claim 9 , wherein the apparatus forms the graphical representation of the set of attributes by:

generating a master tree representation of the set of device classification rules.

14. The apparatus as in claim 13 , wherein the apparatus performs the analysis of the graphical representation of the set of attributes by:

using the master tree representation to determine metrics comprising at least one of:

measures of conflict among the set of device classification rules, measures of stability of classifications by the device classification rules, or measures of time needed to apply the device classification rules to endpoint devices.

15. The apparatus as in claim 14 , wherein the process when executed is further configured to:

optimize at least one of the set of device classification rules based on the metrics.

16. The apparatus as in claim 13 , wherein the set of device classification rules are obtained from a plurality of source systems, and wherein the apparatus provides the result of the analysis to the user interface by:

providing a comparison of the plurality of source systems to the user interface.

17. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:

obtaining, by the device, a set of device classification rules, each device classification rule specifying one or more attributes from a set of attributes and being configured to assign a device type to an endpoint in a network when the endpoint exhibits the one or more attributes specified by that rule;

forming, by the device, a graphical representation of the set of attributes by generating a concept lattice based on the set of attributes that represents a plurality of potential device classification rules, wherein the graphical representation of the set of attributes comprises a plurality of nodes and a plurality of edges interconnecting the plurality of nodes, each node representing a particular subset of the set of attributes being exhibited by one or more endpoints in the network, and each edge representing a relationship between two nodes;

performing, by the device, an analysis of the graphical representation of the set of attributes;

selecting, by the device and based on the analysis of the graphical representation of the set of attributes, a particular device classification rule from among theft plurality of potential device classification rules configured to assign a device type to the one or more endpoints; and

providing, by the device, a result of the analysis to a user interface.

18. The computer-readable medium as in claim 17 , wherein the device type is indicative of one or more of: a manufacturer of the endpoint, a model of the endpoint, or a software version associated with the endpoint.