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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 utilize isolation forest to detect anomalies in the KDD dataset:

  1. Import pandas and read the dataset into a data frame:
import pandas as pdkdd_df = pd.read_csv("kddcup_dataset.csv", index_col=None)
  1. Examine the proportion of types of traffic:
y = kdd_df["label"].valuesfrom collections import CounterCounter(y).most_common()

The following output will be observed:

[('normal', 39247),('back', 1098),('apache2', 794),('neptune', 93),('phf', 2),('portsweep', 2),('saint', 1)]
  1. Convert all non-normal observations into a single class:
def label_anomalous(text):    """Binarize target labels into normal or anomalous."""    if text == "normal":        return 0    else:        return 1kdd_df["label"] = kdd_df["label"].apply(label_anomalous) ...
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Publisher Resources

ISBN: 9781789614671Supplemental Content