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.

利用现有的资产归属数据，研究利用各种机器学习算法确定最佳分类模型以预测资产的所有者，并创建可视化仪表板帮助用户通过交互式探索性数据分析了解资产清单与模型评估指标。

资产所有权识别：利用机器学习预测企业资产所有权