Machine Learning with the KDD and NSL network intrusion detection datasets
- MLbasics/ demonstrates a range of techniques for preprocessing, visualisation, and validation using "toy" datasets from Kaggle
- ML-IDS/ demonstrates the full supervised classification process using scikit Learn - paste in code blocks from the MLbasics notebooks to build it out. NN with tensorflow.keras fits the same pattern: tfNN-base has a full working app, with extra "pop in" blocks in the other notebook.
- mylib/ is a local library with some standard routines, to reduce the clutter in the notebooks
- conda/ has recommended instructions for installing and configuring MiniConda
- datasets/NSL_KDD/ has a "boosted" version of the NSL dataset (see About_KDD-NSL.pdf)
- datasets/original/ has the original datasets, and preprocessed CSV versions (see the notebook) for easy use with the ML-IDS code