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 ...
Get Python Deep Learning now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
