Microsoft Technology Licensing, LLC

Oracle International Corporation

Philip Newman

Puhazholi Vetrivel

Ashish Mishra

Sudhakar Narayanamurthy

Rajesh Basa

Marek Czajka

Roberto Anthony Franco

Girish Nagaraja

Matthew Rushton

Alok Goyal

Ghazanfar Ahmed

Hunt Graham

Chuang Wang

Ejike E Ofuonye

Zheng Guo

Suresh C Palani

Royi Ronen

Elad Yom-Tov

Krishna Kumar Parthasarathy

Hani Hana Neuvirth

Daniel Alon

Martin John Sleeman

Binyan Chen

Ori Kashi

Divya Jain

Weisong Lin

Gagan Arora

Manas Singh

The present embodiments relate to systems and methods for automatic sign in upon account signup. Particularly, the present embodiments can utilize a federated login approach for automatic sign in upon account signup for a cloud infrastructure. Specifically, the signup and sign in service (also known as SOUP) and an identity provider portal can be configured such that the nodes are aware of each other as Security Assertion Markup Language (SAML) partners. After new account registration, the signup service can redirect the user browser to a cloud infrastructure console to start with a federated login flow, where a sign in service can issue a SAML authentication request, and redirects it to signup service. Responsive to validating the browser using a SAML authentication process, the browser can be automatically signed into the new account and allowed access the account relating to the cloud infrastructure service.

Automatic sign-in upon account signup

Systems and methods described herein for providing cross-region resource management for regional infrastructure resources in a cloud infrastructure environment. Systems and methods can provide a cross-region resource management tool that can provide searching, viewing, and management capabilities for any resource in any of the regions to which the cross-region resource management tool has access to. A user (having sufficient access levels, such as an administrator or other privileged user) can access the cross-region resource management tool to view and manage all customer resources in any region of the cloud infrastructure, no matter which region the user is located in.

System and method for cross region resource management for regional infrastructure resources in a cloud infrastructure environment

Systems and methods described herein support compartment quotas in a cloud infrastructure environment. Cloud administrators do not generally have the ability to restrict resource usage in existing clouds. Granting a user permission to create resources allows them to create any number of resources up to a predefined account limit. Compartment quotas allow admins to restrict a user's resource usage to the appropriate level allowing fine-tuned cost control.

System and method for compartment quotas in a cloud infrastructure environment

A computing system includes a processor and memory coupled to the processor and storing instructions that, when executed by the processor provide a user interface module. The user interface module is configured to generate a tracker definition user interface having a threat parameter selection user interface element configured to receive a selection of at least one threat parameter, the tracker definition user interface also having a threat value user interface element configured to receive input specifying a threat value to match for the specified at least one threat parameter. The processor is configured to save a tracker based on the selection of at least one threat parameter and the threat value, and wherein the processor is configured to access a threat data store and execute the tracker against the threat data store to provide a tracker result.

Configurable cyber-attack trackers

Systems and methods described herein for automatic limit service increase in a multi-tenant cloud infrastructure environment. The systems and methods described herein provide for automatic approval of limit service increase requests that are either automatically generated based upon a tenant's usage of resources within the cloud infrastructure environment, or are received via, e.g., a user portal. Such automatic approval can be based upon a set of maximal limits that are computed based upon the tenant's current usage, level of subscription, or hard limit values.

System and method for service limit increase for a multi-tenant cloud infrastructure environment

Malicious activity data is obtained, that is indicative of attempted attacks on a computing system. Clusters of targets are identified and it is determined whether the malicious activity preferentially targets one cluster of targets over other. Also, low prevalence attacks are identified and it is determined whether a low prevalence attack has a high concentration in one or more of the target clusters. If the malicious activity either preferentially targets a cluster, or a low prevalence attack has a high concentration in a cluster, then the attack is identified as a targeted attack, so that remediation steps can be taken.

Detection and identification of targeted attacks on a computing system

Threat intelligence management is provided in a security and compliance environment. A threat explorer platform or module of a security and compliance service may detect, investigate, manage, and provide actionable insights for threats at an organizational level. Working with a data insights platform that collects different types of signals (metadata, documents, activities, etc.) and correlates in a multi-stage evaluation, the threat intelligence module may provide actionable visual information on potential threats, affected areas, and actionable insights derived from internal threat data and external information using contextual correlation of data within the data insight platform. User experience may be dynamically adjusted at multiple levels based on context and allow users to drill down arbitrarily deep.

Threat intelligence management in security and compliance environment

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

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Seattle, WA, United States of America

Philip Newman