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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 this recipe, we detail how to train MalConv on raw PE files:

  1. We import numpy for vector operations and tqdm to keep track of progress in our loops:
import numpy as npfrom tqdm import tqdm
  1. Define a function to embed a byte as a vector:
def embed_bytes(byte):    binary_string = "{0:08b}".format(byte)    vec = np.zeros(8)    for i in range(8):        if binary_string[i] == "1":            vec[i] = float(1) / 16        else:            vec[i] = -float(1) / 16    return vec
  1. Read in the locations of your raw PE samples and create a list of their labels:
import osfrom os import listdirdirectories_with_labels = [("Benign PE Samples", 0), ("Malicious PE Samples", 1)]list_of_samples = []labels = []for dataset_path, label in directories_with_labels: samples = [f for f in ...
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Publisher Resources

ISBN: 9781789614671Supplemental Content