International Business Machines Corporation

Michelle Rivers

Rhonda L Childress

Michael John Spisak

Michael Elton Nidd

Srinivas Babu Tummalapenta

George E Stark

Dorothea W Wiesmann

Carlos E Aguilera

Mariya Ali

Steven D McKay

A security vulnerability analysis mechanism is provided that ingests content from a plurality of content source computing devices to identify instances of security vulnerability content in the ingested content. The mechanism performs a security trend analysis on the instances of security vulnerability content to identify a relative ranking of security vulnerabilities. The mechanism identifies computing resources of a specified computing infrastructure and a criticality of the computing resources to an operation of the computing infrastructure. The mechanism generates a prioritized listing of security vulnerabilities associated with the computing infrastructure based on the relative ranking of security vulnerabilities and the criticality of the computing resources in the computing infrastructure. The mechanism outputs a notification to a user via a user computing device, indicating the prioritized listing of security vulnerabilities.

Cognitive security exposure analysis and resolution based on security trends

Training a random forest model to relate settings of a network security device to undesirable behavior of the network security device is provided. A determination of a corresponding set of settings associated with each region of lowest incident probability is made using a random forest. The plurality of identified desired settings are presented as options for changing the network security device from the as-is settings to the identified desired settings. A choice is received from the plurality of options. The choice informs the random forest model. The random forest model ranks for a new problematic network security device the plurality of options for changing the new problematic network security device from as-is settings to desired settings by aggregating an identified cost of individual configuration changes, thereby identifying a most cost-effective setting for the network security device to achieve a desired output of the network security device.

Deriving optimal actions from a random forest model

Growing community of inventors

Marietta, GA, United States of America

Michelle Rivers