Abstract: Techniques 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 techniques comprising: 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 and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature 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.

Abstract: Techniques for verifying correctness of associations between assets related to events detected in at least one computer network and assets in an asset catalog for the at least one computer network. The techniques include: obtaining information specifying a first asset and a first set of assets with which the first asset was previously associated; generating a signature of the first asset from computer network addressing information for the first asset using at least one trained machine learning model; associating the first asset with a second set of assets using the signature and at least one signature of the at least one asset, wherein the at least one signature was previously determined using the at least one trained machine learning model; and when it is determined that the second set includes the first set, outputting an indication that the first asset was correctly associated with the first set of assets.

Abstract: Techniques for verifying correctness of associations between assets related to events detected in at least one computer network and assets in an asset catalog for the at least one computer network. The techniques include obtaining information specifying a first asset and a first set of assets with which the first asset was previously associated; generating a signature of the first asset from the computer network addressing information for the first asset; generating a hashed signature by applying a locality sensitive hashing (LSH) technique to the signature; associating the first asset with a second set of assets in the asset catalog using the hashed signature and at least one hashed signature of the at least one asset in the asset catalog; and when it is determined that the second set of includes the first set, outputting an indication that the first asset was correctly associated with the first set of assets.

Abstract: Techniques 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 techniques comprising: 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 and at least one signature of the at least one asset in the asset catalog, wherein the at least one signature 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.

Abstract: Techniques 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 techniques include: 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; generating a hashed signature of the first asset by applying a locality sensitive hashing (LSH) technique to the signature; associating the first asset with at least one asset in the asset catalog using the hashed signature of the first asset and at least one hashed signature of the at least one asset in the asset catalog; and outputting information identifying the at least one asset with which the first asset was associated.

Abstract: The invention relates to variants of the human growth gene (GH1) and, in particular, variants in the proximal promoter region thereof. Moreover, the invention relates to the interaction of said variants and how said interaction affects growth hormone expression.