Case Study: Building an Intrusion Detection System on Real Data

Loading the real 1999 KDD Cup dataset directly through scikit-learn, discovering its unrealistic 80 percent attack rate, resampling to a realistic 3 percent, and the honest, unflattering real result of running Isolation Forest out of the box: 95.7 percent accuracy but only 28 percent precision and recall on actual attacks.

~5 min total·4 quadrants of structured content

By the end of this topic, you will

Load the real KDD Cup 99 dataset with scikit-learn's fetch_kddcup99 and check its label distribution
Explain why the raw KDD Cup subset must be resampled before it illustrates realistic intrusion detection
Read a classification report honestly and explain why a high accuracy number can still mean a nearly useless detector

Quadrant 1 · e-Tutorial

Video lectures and walkthroughs

Video content coming soon.

Quadrant 2 · e-Content

Articles and case studies

Case Study: Building an Intrusion Detection System on Real Data

This unit's own real run of Isolation Forest on resampled KDD data scored 95.7 percent accuracy and just 28 percent precision and recall on the attacks that actually mattered. That gap is the single clearest argument in this course for feature engineering.

Quadrant 3 · Web Resources