Chapter 10. Building a Production-Ready Intrusion Detection System

In the previous chapter, we explained in detail what an anomaly detection is and how it can be implemented using auto-encoders. We proposed a semi-supervised approach for novelty detection. We introduced H2O and showed a couple of examples (MNIST digit recognition and ECG pulse signals) implemented on top of the framework and running in local mode. Those examples used a small dataset already cleaned and prepared to be used as proof-of-concept.

Real-world data and enterprise environments work very differently. In this chapter, we will leverage H2O and general common practices to build a scalable distributed system ready for deployment in production.

We will use as an example an intrusion ...

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