Holo-entropy adaptive boosting based anomaly detection

    公开(公告)号:US11620180B2

    公开(公告)日:2023-04-04

    申请号:US16205138

    申请日:2018-11-29

    Applicant: VMware, Inc.

    Abstract: A computer-implemented method for determining whether data is anomalous includes generating a holo-entropy adaptive boosting model using, at least in part, a set of normal data. The holo-entropy adaptive boosting model includes a plurality of holo-entropy models and associated model weights for combining outputs of the plurality of holo-entropy models. The method further includes receiving additional data, and determining at least one of whether the additional data is normal or abnormal relative to the set of normal data or a score indicative of how abnormal the additional data is using, at least in part, the generated holo-entropy adaptive boosting model.

    Holo-entropy based alarm scoring approach

    公开(公告)号:US11258655B2

    公开(公告)日:2022-02-22

    申请号:US16212170

    申请日:2018-12-06

    Applicant: VMware, Inc.

    Abstract: A method for managing alarms in a virtual machine environment includes receiving alarm data related to a process and storing the alarm data in a database, where the alarm data comprises one or more features. The method further includes retrieving intended state information for the process and comparing the one more features of the alarm data to the intended state information to determine whether the alarm is an outlier. The method also includes computing a normal score for the alarm if the alarm is not an outlier, and computing an abnormal score for the alarm if the alarm is an outlier. The method also includes sending a notification for the alarm and the computed score.

    Performing cybersecurity operations based on impact scores of computing events over a rolling time interval

    公开(公告)号:US11689545B2

    公开(公告)日:2023-06-27

    申请号:US17151142

    申请日:2021-01-16

    Applicant: VMware, Inc.

    CPC classification number: H04L63/1416 H04L63/0263 H04L63/1441 H04L63/20

    Abstract: The disclosure herein describes automatically performing security operations associated with a client system based on aggregated event impact scores of computing events during a rolling time interval. Event data is obtained, wherein the event data is from a plurality of computing devices of the client system associated with computing events occurring during a time interval after an endpoint of the rolling time interval. Event impact scores are calculated for the computing events of the obtained event data over the time interval based at least on cardinality estimation. The calculated event impact scores are merged into the set of aggregated event impact scores associated with the rolling time interval and event impact scores associated with an expired time interval are removed from the set of aggregated event impact scores. Based on the set of aggregated event impact scores, at least one security operation is performed for at least one computing event.

    Event-triggered behavior analysis

    公开(公告)号:US11295011B2

    公开(公告)日:2022-04-05

    申请号:US16242396

    申请日:2019-01-08

    Applicant: VMware, Inc.

    Abstract: Certain aspects herein provide a system and method for performing behavior analysis for a computing device by a computing system. In certain aspects, a method includes detecting an event occurring at the computing device at a first time, determining, based on the detecting, an event category of the event, and collecting first one or more behaviors associated with the determined event category occurring on the computing device based. The method also includes comparing the first one or more behaviors with a dataset indicating one or more expected behaviors of the computing device associated with the event. Upon determining that at least one of the first one or more behaviors corresponds to an unexpected behavior based on the comparing, the method further includes taking one or more remedial actions.

    Hierarchical novelty detection using intended states for network security

    公开(公告)号:US11729207B2

    公开(公告)日:2023-08-15

    申请号:US16900240

    申请日:2020-06-12

    Applicant: VMware, Inc.

    Abstract: The disclosure provides an approach for detecting and preventing attacks in a network. Embodiments include determining a plurality of network behaviors of a process by monitoring the process. Embodiments include generating a plurality of intended states for the process based on subsets of the plurality of network behaviors. Embodiments include determining a plurality of intended state clusters by applying a clustering technique to the plurality of intended states. Embodiments include determining a state of the process. Embodiments include identifying a given cluster of the plurality of intended state clusters that corresponds to the state of the process. Embodiments include selecting a novelty detection technique based on a size of the given cluster. Embodiments include using the novelty detection technique to determine, based on the given cluster and the state of the process, whether to generate a security alert for the process.

    Entropy based security detection system

    公开(公告)号:US10860712B2

    公开(公告)日:2020-12-08

    申请号:US16032349

    申请日:2018-07-11

    Applicant: VMware, Inc.

    Abstract: A virtual computing instance (VCI) is protected against security threats by a security manager, monitoring a behavior of a VCI over an observation period. The method further includes, storing by the security manager a digital profile in a first database, wherein the digital profile comprises information indicative of the behavior. The method further includes, accessing by a detection system, the digital profile from the first database, and accessing by the detection system, an intended state associated with VCI, wherein the intended state comprises information indicative of a behavior from a second VCI. The method further includes, comparing at least part of the digital profile to the at least part of the intended state. The method further includes, determining by the detection system, that the VCI contains a security threat when information indicative of a behavior in the digital profile is an outlier.

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