Machine learning techniques for associating assets related to events with addressable computer network assets

1. A method for associating assets related to events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network, the asset catalog assets being addressable on the at least one computer network, the method comprising:

using at least one computer hardware processor to perform:

while monitoring activity on the at least one computer network, obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset;

generating a signature of the first asset from the computer network addressing information using at least one trained machine learning model, wherein the signature comprises a numeric representation of the first asset;

associating the first asset with at least one asset in the asset catalog using the signature of the first asset and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature of the at least one asset was previously determined using the at least one trained machine learning model; and

outputting information identifying the at least one asset with which the first asset was associated.

2. The method of claim 1 , wherein the associating comprises identifying a subset of assets in the asset catalog using a locality sensitive hashing (LSH) technique and comparing the signature of the first asset with signatures of assets in the subset of assets.

3. The method of claim 2 , wherein using the LSH technique comprises applying the LSH technique to the signature of the first asset, the applying comprising applying a min-hash technique to the signature of the first asset.

4. The method of claim 2 , wherein using the LSH technique comprises applying the LSH technique to the signature of the first asset, the applying comprising encoding the signature using a plurality of randomized hyperplanes.

5. The method of claim 1 , wherein the computer network addressing information indicates at least one value for at least one network parameter, the at least one network parameter selected from the group consisting of: a hostname for the first asset on the at least one computer network, an IP address for the first asset on the at least one computer network, and a MAC address for the first asset.

6. The method of claim 1 , wherein generating the signature of the first asset comprises generating a numeric representation of at least some of the computer network addressing information as the numeric representation.

7. The method of claim 6 , wherein generating the numeric representation of the at least some of the computer network addressing information is performed using a character embedding technique.

8. The method of claim 7 , wherein generating the numeric representation of the at least some of the computer network addressing information comprises:

generating an initial numeric representation by applying the character embedding technique to the at least some of the computer network addressing information; and

providing the initial numeric representation as input to the at least one trained machine learning model to obtain the numeric representation,

wherein the numeric representation is a lower-dimensional representation than the initial numeric representation.

9. The method of claim 8 ,

wherein at least one trained machine learning model comprises a plurality of trained machine learning models including a first machine learning model and a second machine learning model,

wherein the initial numeric representation comprises a plurality of portions including a first portion and a second portion, and

wherein the providing further comprises:

providing the first portion of the initial numeric representation as input to a first machine learning model to obtain a corresponding first output;

providing the second portion of the initial numeric representation as input to a second machine learning model to obtain a corresponding second output; and

generating the numeric representation u the first output and second output.

10. The method of claim 8 , wherein the at least one trained machine learning model comprises an autoencoder.

11. The method of claim 1 , wherein the associating comprises calculating a Hamming distance between the signature of the first asset and at least one signature of the at least one asset in the asset catalog.

12. The method of claim 1 , wherein the at least one asset consists of a single asset in the asset catalog.

13. The method of claim 1 , wherein the at least one asset comprises multiple assets in the asset catalog, and wherein the associating comprises:

comparing the signature of the first asset with signatures of each of the multiple assets in the asset catalog; and

associating the first asset with a particular one of the multiple assets based on results of the comparing.

14. The method of claim 1 , wherein the event related to the first asset comprises:

a communication from the first asset, a communication directed to the first asset, or a communication identifying the first asset.

15. The method of claim 1 , wherein the first asset is a physical device addressable on the at least one computer network.

16. The method of claim 1 , wherein the first asset is a virtual device addressable on the at least one computer network.

17. The method of claim 16 , wherein the virtual device is a container or a virtual machine.

18. The method of claim 1 , further comprising:

identifying a policy associated with the identified at least one asset; and

processing the event related to the first asset in accordance with the identified policy.

19. A system for associating events detected in at least one computer network with respective assets in an asset catalog for the at least one computer network, the asset catalog assets being addressable on the at least one computer network, the system comprising:

at least one computer hardware processor; and

at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method comprising:

while monitoring activity on the at least one computer network, obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset;

generating a signature of the first asset from the computer network addressing information using at least one trained machine learning model, wherein the signature comprises a numeric representation of the first asset;

associating the first asset with at least one asset in the asset catalog using the signature of the first asset and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature of the at least one asset was previously determined using the at least one trained machine learning model; and

outputting information identifying the at least one asset with which the first asset was associated.

20. At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method comprising:

while monitoring activity on at least one computer network, obtaining information about an event related to a first asset, the information specifying computer network addressing information for the first asset;

generating a signature of the first asset from the computer network addressing information using at least one trained machine learning model, wherein the signature comprises a numeric representation of the first asset;

associating the first asset with at least one asset in the asset catalog using the signature of the first asset and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature of the at least one asset was previously determined using the at least one trained machine learning model; and

outputting information identifying the at least one asset with which the first asset was associated.