公开(公告)号：US11743282B1

公开(公告)日：2023-08-29

申请号：US17031776

申请日：2020-09-24

Applicant: Amazon Technologies, Inc.

Inventor： MohamadAli Torkamani ,  Baris Coskun ,  Jeffrey Earl Bickford ,  Shane Anil Pereira

IPC: H04L9/40 ,  G06N20/00 ,  H04L61/4511 ,  H04L67/1001 ,  G06F18/211 ,  G06N5/01

CPC classification number: H04L63/1433 ,  G06F18/211 ,  G06N5/01 ,  G06N20/00 ,  H04L61/4511 ,  H04L63/0227 ,  H04L63/1441 ,  H04L67/1001

Abstract: Devices, systems, and methods are provided for cloud-based entity reputation scoring. A method may include determining, based on domain name service (DNS) data associated with entities of the cloud-based environment, a k-partite graph with nodes and edges, a node including a first elastic computing instance. The method may include generating features associated with the first elastic computing instance. The method may include determining, based on the features, a minimum value, a maximum value, and an average value, and generating a feature vector comprising the minimum value, the maximum value, and the average value. The method may include determining, based on the feature vector, a reputation score associated with the first elastic computing instance. The method may include communicating based on the reputation score.

公开(公告)号：US12204645B1

公开(公告)日：2025-01-21

申请号：US17528019

申请日：2021-11-16

Applicant: Amazon Technologies, Inc.

Inventor： MohamadAli Torkamani ,  Bhavna Soman ,  Jeffrey Earl Bickford ,  Baris Coskun

IPC: G06F21/56 ,  G06F21/57 ,  G06N20/20

Abstract: Disclosed are systems and methods to compare two or more machine learning models to determine the comparative performance of those models. Markers may be assigned to data items and data item marker scores generated for those data items, independent of the machine learning models. Each of the machine learning models to be compared may then process the data items and generate respective model scores for those data items. A sub-set of the data items may then be generated for each machine learning model based on the model scores assigned to the data items by the respective model. A model marker score may then be computed for each machine learning model based on the marker scores assigned to each of the data items of the sub-set of data items determined for each model. Finally, the model marker scores may be compared to determine which machine learning model has the highest performance.