Accenture Global Solutions Limited

Accenture Global Services Gmbh

Michael Evan Wendt

Joshua Patterson

Louis William DiValentin

Robin Lynn Burkett

Keith Kraus

Colin Anil Puri

Scott Kurth

Carl Matthew Dukatz

Sonali Parthasarathy

Paul Justin Mahler

Constantine T Boyadjiev

Nathan Shetterley

Vincent Dell'anno

Constantine Boyadjiev

Malicious threat detection through time-series graph analysis, in which a data analysis device receives a data file comprising multiple log data entries. The log data entries include parameters associated with a computer network event in a computing network. The data analysis device produces a graphical model of the computing network based on at least one parameter included in the log data. The data analysis device also identifies a parameter associated with a node of the computer network represented by the graphical model, and performs a time-series analysis on the parameter. The data analysis device further determines, based on the time-series analysis on the parameter, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Malicious threat detection through time series graph analysis

Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.

Graph database analysis for network anomaly detection systems

According to an example, data acceleration may include receiving indications of levels of capabilities respectively needed for data movement, data processing, and data interactivity, and/or operational parameters associated with the data movement, the data processing, and the data interactivity. Data acceleration may further include determining, based on an analysis of the received indications and/or the operational parameters, specifications for the data movement to include streaming and/or batch, the data processing to include a big data platform, complex event processing, and/or an appliance, and the data interactivity to include an in-memory database (IMDB) and/or a distributed cache. Further, data acceleration may include generating, based on the determined specifications, a data acceleration architectural layout to meet the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, and/or the operational parameters associated with the data movement, the data processing, and the data interactivity.

Data acceleration

A network security system detects anomalous network device behavior associated with a network device in a group of similar network devices based on baseline network device behavior determined for the group. A graphical visualization may be generated to analyze the groups.

Network security

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Alexandria, VA, United States of America

Michael Evan Wendt