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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...

In the following steps, we will load a dataset, train a classifier, and then tune a threshold to satisfy a false positive rate constraint:

  1. We load a dataset and specify that the desired FPR is at or below 1%:
import numpy as npfrom scipy import sparseimport scipyX_train = scipy.sparse.load_npz("training_data.npz")y_train = np.load("training_labels.npy")X_test = scipy.sparse.load_npz("test_data.npz")y_test = np.load("test_labels.npy")desired_FPR = 0.01
  1. We write methods to calculate  FPR and TPR:
from sklearn.metrics import confusion_matrixdef FPR(y_true, y_pred):    """Calculate the False Positive Rate."""    CM = confusion_matrix(y_true, y_pred)    TN = CM[0][0]    FP = CM[0][1]    return FP / (FP + TN)def TPR(y_true, y_pred): """Calculate ...
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Publisher Resources

ISBN: 9781789614671Supplemental Content