Machine Learning for Cybersecurity Cookbook

How to do it…

1. Begin by reading in the pickled data:

import picklefile = open('CTU13Scenario1flowData.pickle', 'rb')botnet_dataset = pickle.load(file)

2. The data is already split into train-test sets, and you only need assign these to their respective variables:

X_train, y_train, X_test, y_test = (    botnet_dataset[0],    botnet_dataset[1],    botnet_dataset[2],    botnet_dataset[3],)

3. Instantiate a decision tree classifier with default parameters:

from sklearn.tree import *clf = DecisionTreeClassifier()

4. Fit the classifier to the training data:

clf.fit(X_train, y_train)

5. Test it on the test set:

clf.score(X_test, y_test)

The following is the output:

0.9991001799640072