Hewlett Packard Enterprise Development LP

Life Technologies Corporation

Min-Yi Shen

Douglas Greiner

Jisheng Wang

Mohan Parthasarathy

Brijesh Nambiar

Prasad Palkar

Viswesh Ananthakrishnan

Madhusudana Shashanka

Unum Sarfraz

Chao Yang

Sriram Ramachandran

Systems and methods are provided for managing false positives in a network anomaly detection system. The methods may include receiving a plurality of anomaly reports; extracting fields, and values for the fields, from each of the anomaly reports; grouping the anomaly reports into a plurality of groups according to association rule learning, wherein each group is defined by a respective rule; for each group, creating a cluster based on common values for the fields; and marking each cluster as a possible false positive anomaly cluster.

Managing false positives in a network anomaly detection system

A security appliance with one or more processors and a memory communicatively coupled to the one or more processors is described. The memory includes behavior profiling service logic that, when executed by the one or more processors, (i) creates a behavior profile for a particular entity based on received incoming data, and (ii) determines whether the behavior profile identifies that a malicious attack is being performed by the particular entity based on a comparison of the behavior profile to a reference profile. The reference profile represents historical behavior of the particular entity that is monitored over a prescribed period of time.

System and method directed to behavioral profiling services

Collaborative and adaptive threat intelligence. Data collected on a first customer network is received. One or more local models are trained with at least the received data, where the one or more local models are related to security. An amount of data to transmit to a centralized controller is determined based at least on a result of the training one or more local models and the determined amount of data is transmitted to the centralized controller. Result data is received from the centralized controller that is a result of one or more global models trained on the centralized controller using data collected on multiple customer networks including the first customer network. The one or more local models are adjusted using the received result data and the one or more adjusted local models are trained.

Collaborative and adaptive threat intelligence for computer security

A system for performing quality control for nucleic acid sample sequencing is disclosed. The system comprises a set of solid supports, each solid support having attached thereto a plurality of nucleic acid sequences, wherein the set comprises plural groups of solid supports and each group contains solid supports having the same nucleic acid sequences attached thereto. The nucleic acid sequences of each group differ from each other. The nucleic acid sequences are synthetically derived, and the nucleic acids sequences are designed such that the nucleic acid sequences produce a predefined pattern of detectable signals during a sequencing run. A method of preparing a quality control for performing nucleic acid sample sequencing, a method of validating a nucleic acid sequencing instrument during a nucleic acid sequencing experiment, and a method of processing nucleic acid sequencing data during a nucleic acid sequencing experiment are also disclosed.

Methods and systems for nucleic acid sequencing validation, calibration and normalization

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Palo Alto, CA, United States of America

Min-Yi Shen