Vmware, Inc.

Nicira, Inc.

Bin Zan

Vijay Ganti

Zhen Mo

Gang Xu

Dexiang Wang

Vamsi Krishna Akkineni

Ganesan Chandrashekhar

Calvin Qian

Amit Chopra

Bin Qian

Sanal Pillai

Ruimin Sun

Dan Wing

HengJun Tian

Exemplary methods, apparatuses, and systems include a central controller receiving a request to generate a new encryption key for a security group to replace a current encryption key for the security group. The security group includes a plurality of hosts that each encrypt and decrypt communications using the current encryption key. In response to receiving the request, the central controller determines that a threshold period following generation of the current encryption key has not expired. In response to determining that the threshold period has not expired, the central controller delays execution of the request until the expiration of the threshold period. In response to the expiration of the threshold period, the central controller executes the request by generating the new encryption key, storing a time of creation of the new encryption key, and transmitting the new encryption key to the plurality of hosts.

Minimizing traffic drop when rekeying in a distributed security group

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 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

An approach for an adaptive, performance-oriented, and compression-assisted encryption scheme implemented on a host computer to adaptively improve utilization of CPU resources is provided. The method comprises queueing a new data packet and determining a size of the new data packet. Based on historical data, a plurality of already encrypted data packets is determined. Based on information stored for the plurality of already encrypted data packets, an average ratio of compression for the plurality of already encrypted data packets is determined. Based on the average ratio of compression, a throughput of compression value and a throughput of encryption value, a prediction whether compressing the new data packet will reduce a CPU load is derived. If it is determined that compressing the new data packet will improve utilization of the CPU resources, then a compressed new data packet is generated by compressing the new data packet.

Adaptive, performance-oriented, and compression-assisted encryption scheme

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 embodiments described herein are generally directed to enabling a group of host machines within a network to securely communicate an unknown unicast packet. In some embodiments, a key policy is defined exclusively for the secure communication of unknown unicast packets. The key policy is transmitted by a central controller to the group of host machines for negotiating session keys among each other when communicating unknown unicast packets.

Supporting unknown unicast traffic using policy-based encryption virtualized networks

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.

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Santa Clara, CA, United States of America

Bin Zan