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Machine Learning for Cybersecurity Cookbook
book

Machine Learning for Cybersecurity Cookbook

by Emmanuel Tsukerman
November 2019
Intermediate to advanced content levelIntermediate to advanced
346 pages
9h 36m
English
Packt Publishing
Content preview from 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)
  1. 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],)
  1. Instantiate a decision tree classifier with default parameters:
from sklearn.tree import *clf = DecisionTreeClassifier()
  1. Fit the classifier to the training data:
clf.fit(X_train, y_train)
  1. Test it on the test set:
clf.score(X_test, y_test)

The following is the output:

0.9991001799640072
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Publisher Resources

ISBN: 9781789614671Supplemental Content