Step 1:
Use the 20% subset of the KDDTrain+.txt file from NSL-KDD Dataset located at https://www.unb.ca/cic/datasets/nsl.html for this exercise. Read the information in the URL to guide you in understanding the dataset, then scroll to the end of the page for the ‘download this dataset’ link.
Step 2:
The next page will get you to fill a form; your organization is UMBC and your job title is student. Download the NSL-KDD zip file, unzip, and locate KDDTrain+_20Percent.TXT (you can change the extension to .csv)
Step 3
Create 2 training sets by selecting samples from this data set and evaluate them using decision trees (such as J48 in Weka). You can use random sampling or any other selective sampling technique. Compare the decision trees you find and describe any key changes between the trees.
Comment on why these changes may be occuring by looking at the class distribution in your samples or the size of your training samples.
You may use alternate analysis techniques such as clustering and associations to supplement your analysis (although this is not required).
Submit a word document of your assignment, please make sure to include the decision tree snapshots and other relevant snapshots in your assignment. You do not need to include snapshots of every intermediate step or analysis.
You can use weka or any other alternative data mining tool for this assignment.
ATTACHMENTS
assignment1_sample_solution_1_.pdf
details_on_the_assignment.pdf
solution_guide.txt
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