Unisys Corporation

Michael Di Domenico

Michael C Leap

Robert A Johnson

Edward T Cavanagh

Philippe Jolly

Richard W Phelps

Brittney Burchett

Emily M Shoup

Dmitriy Yarmaliuk

Cody Finn

Methods and systems for configuring a common security policy for a plurality of nodes included within an enterprise network. Example methods can include grouping nodes within profiles based on IP address, in addition to concordance data. Additionally, nodes may be added to profiles based on a classification of the node being common to classifications of nodes within the profile. Still further, profiles may be grouped into a solution based at least in part on classification of the profile, in addition to grouping of profiles into solutions based on affinitization using concordance data. The methods described also include determining a common security policy to apply to each of the nodes within the profile.

Object scope definition for enterprise security management tool

Methods and systems for configuring a security policy for an enterprise within an enterprise security management tool are disclosed. In some aspects, such systems receive a definition of at least one custom classification within a user interface of the enterprise security management configuration tool, including a name of a profile and network activity associated with one or more nodes to be included within the profile. Such systems also generate a security settings file to be applied within the enterprise, the security settings file including, for each profile, a common security policy to each of the nodes included in the profile. The profiles to which the security settings file is applied include the profile defined by the at least one custom classification.

Custom node and profile classifications for enterprise security management tool

Methods and systems for providing an emergency incident response are described. Data is received from a plurality of heterogeneous data feeds each having a native format and including one or more publically-available data feeds. Event data is extracted from the received data, the event data representing one or more events from one or more of the plurality of heterogeneous data feeds, and the event data is translated from the native format into a standardized analysis format. Further, an existence and a priority level of at least one emergency incident contemporaneous with the event data and based at least in part on the event data is determined, the priority level being selected from among a plurality of predefined priority levels, and a graphical user interface including a message describing the at least one determined emergency incident is displayed.

Emergency incident detection platform

Methods and systems for determining public events are described. Data is received from a plurality of heterogeneous data feeds each having a native format. Event data objects are generated based on the received data, the event data objects each including at least a human readable message, an event time, and an event location. The human readable message of each of the event data objects is parsed into keywords, and the event data objects are grouped into one or more public events based on the keywords, time, location, and a sentiment based on the keywords. The public events are filtered based on a filter score, and the event data objects are clustered based on sentiment, location, and time. A density of the clustered event data objects is determined, and the validity of the public events is determined by a comparison of the density to a predetermined density threshold.

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Malvern, PA, United States of America

Michael Di Domenico