Microsoft Technology Licensing, LLC

International Business Machines Corporation

Naama Kraus

Tamer Salman

Roy Levin

Andrey Karpovsky

Yotam Livny

Moshe Israel

Moshe Shalala

Rotem Lurie

Avihai Dvir

Tomer Rotstein

Fady Nasereldeen

Michael Edward Factor

Idan Yehoshua Hen

Benjamin Mandler

Ron Matchoro

Oran Brill

Assaf Israel

Haim Saadia Ben Danan

Salam Bashir

According to examples, an apparatus may include a memory on which is stored machine-readable instructions that may cause a processor to determine, for each of a plurality of members in a group, a respective least privilege level for a resource and determine, based on the determined respective least privilege levels, a privilege level to be assigned to the group for the resource. The instructions may also cause the processor to assign the determined privilege level to the group for the resource and apply the assigned privilege level to the members of the group for the resource.

Privilege level assignments to groups

Unauthorized use of user credentials in a network is detected. Data indicative of text strings being used to access resources in the network is accessed. Regex models are determined for the text strings. Troupings of the regex models are determined based on an optimization of a cumulative weighted function. A regex model having a cumulative weighted function that exceeds a predetermined threshold is identified. An alert is generated when the cumulative weighted function for the identified regex model exceeds the predetermined threshold.

Detection of abnormal entities based on syntactic grouping of strings preliminary class

According to examples, an apparatus may include a memory on which is stored machine-readable instructions that may cause a processor to identify a privilege level assigned to a principal over a resource and determine whether the assigned privilege level is to be maintained or modified for the principal over the resource. Based on a determination that the assigned privilege level is to be maintained for the principal, the processor may determine whether access by the principal over the resource is to be limited and based on a determination that access to the resource is to be limited, apply a limited access by the principal over the resource.

Modify assigned privilege levels and limit access to resources

Cybersecurity and data categorization efficiency are enhanced by providing reliable statistics about the number and location of sensitive data of different categories in a specified environment. These data sensitivity statistics are computed while iteratively sampling a collection of blobs, files, or other stored items that hold data. The items may be divided into groups, e.g., containers or directories. Efficient sampling algorithms are described. Data sensitivity statistic gathering or updating based on the sampling activity ends when a specified threshold has been reached, e.g., a certain number of items have been sampled, a certain amount of data has been sampled, sampling has used a certain amount of computational resources, or the sensitivity statistics have stabilized to a certain extent. The resulting statistics about data sensitivity can be utilized for regulatory compliance, policy formulation or enforcement, data protection, forensic investigation, risk management, evidence production, or another classification-dependent or classification-enhanced activity.

Data security classification sampling and labeling

An anomalous user session detector is disclosed. A sequence of operations in a logon session for an authorized user is gathered. A supervised learning model is trained to identify the authorized user from the sequence of operations. An anomalous session is detected by querying the supervised learning model.

Anomalous user session detector

Unauthorized use of user credentials in a network is detected. Data indicative of text strings being used to access resources in the network is accessed. Regex models are determined for the text strings. Groupings of the regex models are determined based on an optimization of a cumulative weighted function. A regex model having a cumulative weighted function that exceeds a predetermined threshold is identified. An alert is generated when the cumulative weighted function for the identified regex model exceeds the predetermined threshold.

Detection of abnormal entities based on syntactic grouping of strings

Generally discussed herein are devices, systems, and methods for computer or other network device security. A method can include identifying a profile associated with event data regarding an operation performed on a cloud resource, determining whether the event data is associated with anomalous customer interaction with the cloud resource, in response to determining the event data is associated with anomalous customer interaction, identifying whether another cloud resource of the cloud resources with a lower granularity profile that is associated with the profile of the cloud resource has previously been determined to be a target of an anomalous operation, and providing a single alert to a client device indicating the anomalous behavior on the cloud resource in response to determining both the event data is associated with anomalous customer interaction and the another cloud resource is determined to be the target of the anomalous operation.

Cloud security using multidimensional hierarchical model

Anomalous sequences are detected by approximating user sessions with heuristically extracted event sequences, allowing behavior analysis even without user identification or session identifiers. Extraction delimiters may include event count or event timing constraints. Event sequences extracted from logs or other event lists are vectorized and embedded in a vector space. A machine learning model similarity function measures anomalousness of a candidate sequence relative to a specified history, thus computing an anomaly score. Restrictions may be placed on the history to focus on a particular IP address or time frame, without retraining the model. Anomalous sequences may generate alerts, prompt investigations by security personnel, trigger automatic mitigation, trigger automatic acceptance, trigger tool configuration actions, or result in other cybersecurity actions.

Dynamic cybersecurity detection of sequence anomalies

Tools and techniques are described to automate triage of security and operational alerts. Insight instances extracted from raw event data associated with an alert are aggregated, vectorized, and assigned confidence scores through classification based on machine learning. Confidence scoring enables heavily loaded administrators and controls to focus attention and resources where they are most likely to protect or improve the functionality of a monitored system. Feature vectors receive a broad base in the underlying instance values through aggregation, even when the number of instance values is unknown prior to receipt of the event data. Visibility into the confidence scoring process may be provided, to allow tuning or inform further training of a classifier model. Performance metrics are defined, and production level performance may be achieved.

Enhancing cybersecurity and operational monitoring with alert confidence assignments

A method for enforcing computer-based file system security, the method comprising generating a content-based file system from files in a physical file system, and enforcing a user access right to any aspect of the content-based file system, where the user access right derives from a user access right to a file in the physical file system.

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Haifa, Israel

Naama Kraus