Post-lab

The knowledge gained from the pre-lab and hands-on lab in this module allows students to further explore the practical use of Intrusion Detection Systems (IDS), specifically the Anomaly-based approach.

Anomaly-based IDS:

Recall in the pre-lab of this module, we analyzed the differences between Signature-based IDS and Anomaly-based IDS. These algorithms are particularly helpful when used in a virtual network. The hands-on lab for IDS has only demonstrated signature-based IDS. 

Using your knowledge in the Anomaly-based Module and the extra information found in the pre-lab for the IDS Module, demonstrate normal anomaly-based IDS using a neural network. In other words, find a dataset and spot outliers with anomaly detection and Generative AI. You are not demonstrating network traffic anomalies in this module. You are demonstrating dataset anomaly detection. You may use Google Colab to implement these requirements.