Amazon Technologies, Inc.

Jeffrey Earl Bickford

Baris Coskun

MohamadAli Torkamani

Shane Anil Pereira

Bhavna Soman

Abhinandan Dubey

Christopher Stinson

Jiahao Ding

Disclosed are systems and methods that monitor for malicious and unauthorized behaviors, determine categories for detected malicious behaviors, determine why a domain is determined to be malicious, and provide information to users that identifies the categories and reasons as to why a domain is determined to be malicious. In some implementations, the disclosed systems and methods may be utilized to provide monitoring security to customers of a cloud service. For example, customers of a cloud service may maintain an account with the cloud service and the disclosed implementations may be utilized to protect those accounts from malicious attacks and cybercrimes such as, but not limited to, spam, phishing, malware, botnets, etc.

Detection of malicious domains

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.

Machine learning model evaluation and comparison

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.

Enhanced cloud-based entity reputation scoring

Growing community of inventors

Thornton, CO, United States of America

Jeffrey Earl Bickford