Microsoft Technology Licensing, LLC

Daniel Alon

Royi Ronen

Elad Yom-Tov

Yair Tor

Mark Fishel Novak

Efim Hudis

Hani Hana Neuvirth

Hani Neuvirth-Telem

Yoav Yassour

Amit Hilbuch

Yehuda Finkelstein

Shay Baruch Nahum

Moshe Israel

Khaja Ehteshamuddin Ahmed

Yuri Gurevich

Eugene (John) Neystadt

Philip Newman

Hanan Shteingart

Tomer Teller

Eugene John Neystadt

Amos Ortal

Gilad Michael Elyashar

Alexey Efron

Ori Kashi

Ran Didi

Michal Braverman-Blumenstyk

Hani-Hana Neuvirth

A recommendation system for recommending a target feature value for a target feature for a target deployment is provided. The recommendation system, for each of a plurality of deployments, collects feature values for the features of that deployment. The recommendation system then generates a model for recommending a target feature value for the target feature based on the collected feature values of the features for the deployments. The recommendation system applies the model to the features of the target deployment to identify a target feature value for the target feature. The recommendation system then provides the identified target feature value as a recommendation for the target feature for the target deployment.

Automatic recommendations for deployments in a data center

Systems and methods for analyzing security alerts within an enterprise are provided. An enterprise graph is generated based on information such as operational intelligence regarding the enterprise. The enterprise graph identifies relationships between entities of the enterprise and a plurality of security alerts are produced by a plurality of security components of the enterprise. One or more significant relationships are identified between two or more of the plurality of security alerts based on a strength of a relationship identified in the enterprise graph. A significant relationship is utilized to identify a potential security incident between two or more of the security alerts.

Enterprise graph method of threat detection

Controlling device security includes obtaining a set of device activity data indicating current device activity on a device and a set of user activity data indicating a current activity state of one or more legitimate users of the device. It is determined whether the indicated current activity state of the users indicates that a legitimate user is in an active state on the device, or that none of the legitimate users is in an active state on the device. A statistical fit of the indicated current device activity on the device, with the indicated current activity state of the one or more legitimate users, is determined, by a comparison with at least one of the models that are generated via supervised learning. A security alert action may be initiated, based on a result of the determination of the statistical fit indicating a compromised state of the device.

Detection of compromised devices via user states

A system for identifying abnormal resource usage in a data center is provided. In some embodiments, the system employs a prediction model for each of a plurality of resources and an abnormal resource usage criterion. For each of a plurality of resources of the data center, the system retrieves current resource usage data for a current time and past resource usage data for that resource. The system then extracts features from the past resource usage data for that resource, predicts using the prediction model for that resource usage data for the current time based on the extracted features, and determines an error between the predicted resource usage data and the current resource usage data. After determining the error data for the resources, the system determines whether errors satisfy the abnormal resource usage criterion. If so, the system indicates that an abnormal resource usage has occurred.

Detection of abnormal resource usage in a data center

In an example embodiment, a computer-implemented method comprises obtaining labels from messages associated with an email service provider, wherein the labels indicate for each message IP how many spam and non-spam messages have been received; obtaining network data features from a cloud service provider; providing the labels and network data features to a machine learning application; generating a prediction model representing an algorithm for determining whether a particular set of network data features are spam or not; applying the prediction model to network data features for an unlabeled message; and generating an output of the prediction model indicating a likelihood that the unlabeled message is spam.

Spam classification system based on network flow data

Embodiments of the invention make the issuance of trustworthy device claims available to client devices as a service, so that a client device to which device claims are issued may use the device claims in relation to an attempt to access a network application. The service may conduct an assessment of the device's characteristics and/or state, characterize the results of this assessment in device claims, and issue the device claims to the device. The service may be accessible to a client device from outside administrative boundaries of an entity that makes a network application accessible, and thus may be useful to entities making network applications accessible in business-to-consumer (B2C) and business-to-business (B2B) topologies, such as over the publicly accessible Internet.

Trustworthy device claims as a service

Embodiments of the invention enable a client device to procure trustworthy device claims describing one or more attributes of the client device, have those device claims included in a data structure having a format suitable for processing by an application, and use the data structure which includes the device claims in connection with a request to access the application. The application may use the device claims to drive any of numerous types of application functionality, such as security-related and/or other functionality.

Trustworthy device claims for enterprise applications

Growing community of inventors

Tel Mond, Israel

Daniel Alon