The expansion of the internet of things (IoT) across the world has created security concerns for organizations and consumers alike. Numerous researchers have shown that IoT vulnerabilities are increasing and that tools should be used to stop them or detect them. This paper explores the use of data analysis to help organizations manage the security of IoT networks using the National Institute of Standards and Technology (NIST) Risk Management Framework (RMF). This paper does not concentrate on the end tool, but rather demonstrates the ability to analyze IoT traffic and discern whether or not an attack has been carried out or is in progress. We focus on the `monitor' portion of the framework and demonstrate data analysis of whether an attack has been carried out. We use machine learning and analytical tools to accomplish this analysis. Knowing what the attack looks like allows for practical applications of toolsets.