Skip to Content
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 show three different methods for selecting the most informative N-grams. The recipe assumes that binaryFileToNgramCounts(file, N) and all other helper functions from the previous recipe have been included:

  1. Begin by specifying the folders containing our samples, specifying our N, and importing modules to enumerate files:
from os import listdirfrom os.path import isfile, joindirectories = ["Benign PE Samples", "Malicious PE Samples"]N = 2
  1. Next, we count all the N-grams from all the files:
Ngram_counts_all_files = collections.Counter([])for dataset_path in directories:    all_samples = [f for f in listdir(dataset_path) if isfile(join(dataset_path, f))]    for sample in all_samples: file_path = join(dataset_path, ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Hands-On Machine Learning for Cybersecurity

Hands-On Machine Learning for Cybersecurity

Soma Halder, Sinan Ozdemir
Machine Learning on Kubernetes

Machine Learning on Kubernetes

Faisal Masood, Ross Brigoli

Publisher Resources

ISBN: 9781789614671Supplemental Content