  Asset owner identification is an important first step for any information
security organization, allowing organizations the ability to identify and
detect data breaches and losses, vulnerabilities, possible attack surfaces, and
define effective countermeasures. Using existing asset ownership data, the
research utilized an assortment of machine learning algorithms to determine the
best classification model to predict an asset's owner. The research ran
separate analyses for each enumerated team, then ran a 100 iteration Monte
Carlo Cross Validation across Adaboost, Logistic Regression, Naive Bayes,
Classification and Regression Trees, and Random Forests. Finally, a
visualization dashboard was created to help users understand the asset
inventory through interactive exploratory data analysis as well as the ability
to understand model evaluation metrics including accuracy, sensitivity, and
specificity for each model. Overall, Adaboost performed the best across all
owners with low testing errors below 5% while Naive Bayes performed the worst.
The remaining models performed similarly. The fully qualified domain name
(FQDN), Classless Inter-Domain Routing (CIDR) CIDR/16, and location were among
the most important features.
