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公开(公告)号：US20210019577A1
公开(公告)日：2021-01-21
申请号：US16514059
申请日：2019-07-17
Applicant: VMware, Inc.
Inventor： Bin ZAN , Zhen MO , Vijay GANTI , Vamsi Krishna AKKINENI
Abstract: A feature selection methodology is disclosed. In a computer-implemented method, components of a computing environment are automatically monitored, and have a feature selection analysis performed thereon. Provided the feature selection analysis determines that features of the components are well defined, a classification of the features is performed. Provided the feature selection analysis determines that features of the components are not well defined, a similarity analysis of the features is performed. Results of the feature selection methodology are generated.
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公开(公告)号：US20200019698A1
公开(公告)日：2020-01-16
申请号：US16032349
申请日：2018-07-11
Applicant: VMware, Inc.
Inventor： Zhen MO , Dexiang WANG , Bin ZAN , Vijay GANTI , Amit CHOPRA
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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公开(公告)号：US20210019422A1
公开(公告)日：2021-01-21
申请号：US16514042
申请日：2019-07-17
Applicant: VMware, Inc.
Inventor： Bin ZAN , Zhen MO , Vijay GANTI , Vamsi Krishna AKKINENI
Abstract: A feature selection methodology is disclosed. In a computer-implemented method, the feature selection methodology automatically monitors components of a computing environment. The feature selection methodology then determines the importance of various components of the computing environment. The feature selection methodology further outputs results of the determining of the importance of the components within the computing device.
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Patent Agency Ranking
公开(公告)号：US20200218800A1
公开(公告)日：2020-07-09
申请号：US16242396
申请日：2019-01-08
Applicant: VMware, Inc.
Inventor： Ruimin SUN , Vijay GANTI , Zhen MO , Bin ZAN , Vamsi AKKINENI
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 comprises taking one or more remedial actions.
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公开(公告)号：US20200174867A1
公开(公告)日：2020-06-04
申请号：US16205138
申请日：2018-11-29
Applicant: VMware, Inc.
Inventor： Zhen MO , Bin ZAN , Vijay GANTI , Vamsi AKKINENI , HengJun TIAN
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.
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公开(公告)号：US20200059482A1
公开(公告)日：2020-02-20
申请号：US16103108
申请日：2018-08-14
Applicant: VMware, Inc.
Inventor： Bin ZAN , Dexiang WANG , Zhen MO MO , Vijay GANTI
Abstract: Feature vectors are abstracted from data describing application processes. The feature vectors are grouped to define non-anomalous clusters of feature vectors corresponding to normal application behavior. Subsequent feature vectors are considered anomalous if they do not fall within one of the non-anomalous clusters; alerts are issued for anomalous feature vectors. In addition, the subsequent feature vectors may be used to regroup feature vectors to adapt to changes in what constitutes normal application behavior.
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公开(公告)号：US20220027409A1
公开(公告)日：2022-01-27
申请号：US16937417
申请日：2020-07-23
Applicant: VMware, Inc.
Inventor： Srilakshmi LINGAMNENI , Barak RAZ , Bin ZAN , Zhen MO , Vijay GANTI
IPC: G06F16/901 , G06F16/28 , H04L12/24 , H04L29/12 , G06K9/62
Abstract: An example method of representing a selected entity in a plurality of entities in a computing system includes: obtaining a graph representation of the plurality of entities, the graph representation having nodes and edges representing a hierarchy of the plurality of entities; extracting a set of paths from the graph representation, each path in the set of paths including a series of edge-connected nodes in the graph representation; processing the set of paths to generate a vector representation of the selected entity, the vector representation having a plurality of elements representing a context of the selected entity within the graph representation; and providing the vector representation as input to an application executing in the computing system.
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公开(公告)号：US20200186409A1
公开(公告)日：2020-06-11
申请号：US16212170
申请日：2018-12-06
Applicant: VMware, Inc.
Inventor： Zhen MO , Dexiang WANG , Bin ZAN , Vijay GANTI , Amit CHOPRA , Ruimin SUN
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.
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