According to The CERT Division of SEI (n. d.) and Jazzar (2013) “The best way for administrators to protect their networks is to monitor and analyze their network traffic. Understanding the traffic can help them characterize threats and attacks, and it can also help them identify vulnerabilities in their networks. However, processing traffic on large networks can be time-consuming and expensive, and it may be impossible without effective automation tools…” (p. 4). IDS is an effective network traffic analyzer and defense tool that can analyze and identify vulnerabilities, as well as detect intrusion, exploits, and hostile activities on the system network. This study attempts to support the current IDS by supplementing with an inference monitor system that works in unsupervised learning mode, which can provide adoption, integrity, and an information-sharing platform among the IDS components.