Vmware, Inc.

Nicira, Inc.

Zhen Mo

Vijay Ganti

Bin Zan

Vamsi Krishna Akkineni

Dexiang Wang

Amit Chopra

Ruimin Sun

Barak Raz

Debessay Fesehaye Kassa

Bo Hu

Helen Liu

HengJun Tian

Ereli Eran

Honglei Li

Fang Peng

Patrick Charles Upatham

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.

Security in a computing environment by automatically defining the scope and services of components within the computing environment

A method of generating relevant security rules for a user includes the steps of: building a first tree data structure from paths within a pool of security rules; collecting process paths for the user; and compiling the relevant security rules for the user by traversing the first tree data structure according to the process paths of the user.

System and method for matching, grouping and recommending computer security rules

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.

Feature selection using term frequency-inverse document frequency (TF-IDF) model

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.

Hierarchical novelty detection using intended states for network security

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.

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

Machine learning-based techniques for representing computing processes as vectors are provided. In one set of embodiments, a computer system can receive a name of a computing process and context information pertaining to the computing process. The computer system can further train a neural network based on the name and the context information, where the training results in determination of weight values for one or more hidden layers of the neural network. The computer system can then generate, based on the weight values, a vector representation of the computing process that encodes the context information and can perform one or more analyses using the vector representation.

Machine learning-based techniques for representing computing processes as vectors

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 adaptive boosting based anomaly detection

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.

Event-triggered behavior analysis

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.

Holo-entropy based alarm scoring approach

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.

Adaptive anomaly detection for computer systems

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.

Entropy based security detection system

Certain embodiments described herein are generally directed to methods and apparatus for providing a security parameter index (SPI) value for use in establishing a security association between a source tunnel endpoint and a destination tunnel endpoint. In some embodiments, utilization of the SPI bit space is optimized to allow the scaling of key policies within a network. In some embodiment, using an SPI derivation formula, a server in the network is able to generate SPI values whose bit spaces are optimized to allow key policies to scale out.

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Sunnyvale, CA, United States of America

Zhen Mo