A research & development project to create and deploy a Network-based Intrusion Detection System (IDS) to detect intruders on a distributed system. That is, it detects and classify threatening or anomalous network traffic as opposed to safe traffic and usage. The project runs on a real-time, distributed cluster on Apache Storm which processes incoming network packets, and uses our novel algorithms and Machine Learning to detect intruders. It uses supervised Machine Learning classifiers such as decision trees, ensemble decision trees, support vector machines, etc. as well as being built with the principles of anomaly-based Intrusion Detection Systems.