Dataset_1 and Dataset_2 are logfiles from an intrusion detection system (IDS).
Basic Data Processing, Analysis and Visualization are done using Python Pandas package. Histograms are generated to visualise the results.
Clustered the Source and Destination IP addresses by the number of records they appeared in. Different clustering algorithms like K-means, Hierarchical, and Gaussian Mixture clustering are explored and implented. Elbow method is used for determining the number of clusters.
Identified relation between source and destination clusters and illustrated graphically with their conditional probabilities.
Finally, a decision tree is learnt using the 2 features (i.e. the source cluster and the destination cluster) to predict the classification field. Repeated the same with Dataset_2.
This developed on Jupyter notebook with Python 3.x. Run the DecisionTree.ipynb file using Jupyter notebook. In addition, a python file DecisionTree.py can work outside jupyter notebook.
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