As machine learning (ML) continues to support technological systems, the number of ways such systems can be compromised to cause unwanted behavior increases. Splunk, a cybersecurity monitoring tool, is also subject to advanced persistent threats (APTs) seeking to alter ML-powered security tools to evade detection. This research determines how effectively a HIDS (host-based intrusion detection system) can alert data integrity loss within the Splunk machine learning model to help security personnel detect an APT.