Microsoft Technology Licensing, LLC

Michael Zeev Bargury

Moshe Israel

Ben Kliger

Yotam Livny

Roy Levin

Ram Haim Pliskin

Mathias Abraham Marc Scherman

Gal Malka

Dotan Patrich

Efim Hudis

Gilad Michael Elyashar

Pnina Weiss

An automatic code analysis tool is disclosed. The automatic code analysis tool can automatically analyze an application code of a subject application and application-specific additional information of a resource used by the subject application. The automatic code analysis tool can implement a method to receive the application code for analysis. A determination is made as to how application-specific information from resources operably coupled to the application code is implemented via the application code. The determination is performed with an automatic analysis without running the application code.

Automated code analysis tool

Privately determining whether a password satisfies a constraint without having to divulge the password itself to a third party that evaluates the constraint, and without the third party even being aware of the result of the evaluation. After the user selects a password, private communication (e.g., private information retrieval) is used to determine whether the selected password satisfies password constraints. For instance, the password might be encrypted (e.g., homomorphically), and then the encrypted password and a function definition (e.g., a homomorphic function definition) is then provided to the third party. The third party then performs the function and returns an already encrypted result. The third party generated the encrypted result directly, without having access to the result in the clear. Upon receiving the encrypted result, the user's computing system may then decrypt the result, to find out whether the password satisfies the constraints, and thus is sufficiently safe.

Private password constraint validation

A security service utilizes a machine learning model to detect unused open ports. A security agent on client machines tracks the operating executables and the open ports on a machine. A machine learning model is trained for a specific port number using the more commonly-used executables that run on machines having the port opened from a large and diverse population of machines. The model is then used to determine the ports that an executable is likely to be associated with which is then used to determine if a particular machine has an unused open port.

Machine learning detection of unused open ports

The least-privilege permission needed for an identity, such as a user account, application, user group, or process, to access a resource of a tenant of a cloud service is determined from a predicted future resource usage. The predicted future resource usage is based on the resource usage history of an identity, the resource usage history of similar identities and the resource usage history of its peers. Similar identities are determined from node embeddings of a graph that represents the assigned permissions of an identity to a resource and the usage activity at a resource. The permissions needed to perform the predicted future resource usage is compared with the current permission assignments to determine the bare minimum permission that an identity needs for its ongoing and future workflow.

Least-privilege resource permission management

A correlation-based network security for network devices is disclosed. Correlations between a plurality of network devices are mapped based on telemetry from the network devices to determine correlated devices. The behaviors of the correlated devices are monitored based on telemetry received from the correlated devices to determine a deviant device of the plurality of devices. A prioritized alert for the plurality of network devices is generated from a security alert received for the deviant device.

Correlation-based network security

Systems, methods, and apparatuses are provided for clustering incidents in a computing environment. An incident notification relating to an event (e.g., a potential cyberthreat or any other alert) in the computing environment is received and a set of features may be generated based on the incident notification. The set of features may be provided as an input to a machine-learning engine to identify a similar incident notification in the computing environment. The similar incident notification may include a resolved incident notification or an unresolved incident notification. An action to resolve the incident notification may be received, and the received action may thereby be executed. In some implementations, in addition to resolving the received incident notification, the action may be executed to resolve a similar unresolved incident notification identified by the machine-learning engine.

Similarity based approach for clustering and accelerating multiple incidents investigation

Methods, systems, and media are shown for generating access control rules for computer resources involving collecting historical access data for user accesses to a computer resource and separating the historical access data into a training data set and a validation data set. An access control rule is generated for the computer resource based on the properties of the user accesses to the computer resource in the training data set. The rule is validated against the validation data set to determine whether the rule produces a denial rate level is below a threshold when the rule is applied to the validation data set. If the rule is valid, then it is provided to an administrative interface so that an administrator can select the rule for application to incoming user requests.

Automated access control policy generation for computer resources

A system for predicting vulnerability of network resources is provided. The system can calculate an initial vulnerability score for each of the network resources and use the initial vulnerability scores along with activity data of the network resources to train a vulnerability model. After training, the vulnerability model can predict the vulnerability of the network resources based on new activity data collected from the network resources. Based on the predicted vulnerability, vulnerable network resources can be identified. Further analysis can be performed by comparing the activities of the vulnerable network resources and other network resources to identify activity patterns unique to the vulnerable network resources as attack patterns. Based on the attack patterns, one or more actions can be taken to increase the security of the vulnerable network resources to avoid further vulnerability.

Increasing security of network resources utilizing virtual honeypots

A computing system performs real-time mitigations for unfamiliar threat scenarios by identifying a particular threat scenario for a client system that has not previously experienced the threat scenario and for which a remediation process is unknown. The computing system responds to the unknown threat scenario by generating and providing the client system a mitigation file that includes a predictive set of mitigation processes for responding to the threat scenario. The mitigation file is generated by first generating a threat vector that identifies a plurality of different threat scenario characteristics for the particular threat scenario. Then, a classification model is applied to the threat vector to identify a predictive set of mitigation processes that are determined to be a best fit for the threat vector and that are included in the mitigation file.

Real-time mitigations for unfamiliar threat scenarios

Control policies are configured to automatically update a whitelist and to permit an application, including its associated computing operations, to execute on the computer system. After the application is installed, initialization and execution of the application is triggered. Concurrently, the application's computing operations are recorded and certain control policies, such as a firewall, are paused from being enforced. The recorded computing operations are classified into at least two different categories, where one category includes computing operations associated with the application and where another category includes computing operations that are not associated with the application but that occurred while the application was running. The first category computing operations are then whitelisted so that they are identified as being permissible computing operations by the control policies. Thereafter, the control policies are again enforced thus allowing the application, including its associated computing operations, to execute without being interrupted by the control policies.

Listen mode for machine whitelisting mechanisms

A computing system utilizes crowd sourcing to generate remediation files for systems experiencing alert conditions. During the generation of the remediation files the computing system identifies a plurality of different types of alerts associated with a plurality of different client systems. The computing system also generates a plurality of different client remediation process sets for each type of alert based on a correlation of process proximity and time to the alert conditions and determines which of the plurality of processes are related to the identified alert based on values in a correlation vector. Then, client remediation process sets are created to include the processes that are determined to be related to the identified alert and are clustered together to identify the processes to include in the generated composite remediation file for each type of alert, based on correlations existing between the plurality of different client remediation process sets.

Automatic generation of threat remediation steps by crowd sourcing security solutions

A security configuration for a firewall is generated. Network traffic data, network reputation data, and endpoint protection data are received from a network environment. A reputation score for a network address is generated from the network traffic data and the network reputation data. An endpoint protection configuration is generated from a routine based on the network traffic data and the endpoint protection data. A set of security rules is provided from the endpoint configuration and the reputation score.

Firewall configuration manager

Described technologies automatically detect candidate networks having external nodes which communicate with nodes of a local network; a candidate external network can be identified even when the external nodes are owned by a different entity than the local network's owner. A list of network addresses which communicated with local network nodes is culled to obtain addresses likely to communicate in the future. A graph of local and external nodes is built, and connection strengths are assessed. A candidate network is identified, based on criteria such as connection frequency and duration, domain membership, address stability, address proximity, and others, using cutoff values that are set by default or by user action. The candidate network identification is then utilized as a basis for improved security though virtual private network establishment, improved bandwidth allocation, improved traffic anomaly detection, or network consolidation, for example.

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Ramat Gan, Israel

Michael Zeev Bargury