Accenture Global Solutions Limited

Accenture Global Services Gmbh

Joshua Patterson

Michael Evan Wendt

Louis William DiValentin

Robin Lynn Burkett

Keith Kraus

Paul Justin Mahler

Constantine T Boyadjiev

Nathan Shetterley

Brad H Ruderman

Shan Lu

Brad Ruderman

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

A system acquires geocoded data, smoothes the geocoded data, and displays the smoothed geocoded data in a graphical visualization representative of the physical locations of the acquired geocoded data. The system obtains an indication of operational parameters for smoothing geocoded data, such as a data precision level, viewing window, data characteristics, and smoothing parameters. The system generates an area map that includes entries for each location in the viewing window (or other predetermined area) according to the data precision level. The system may populate any entries without associated data values with a zero value. To process the geocoded data, the system may determine a vector of interest from the area map and generate, for example, a single smoothing matrix that smoothes each of the values in the vector of interest. The system applies the smoothing matrix to the vector of interest.

Geospatial smoothing in web applications

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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Joshua Patterson