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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 it works…

We begin by importing numpy and tqdm (Step 1), a package that allows you to keep track of progress in a loop by showing a percentage progress bar. As part of feeding the raw bytes of a file into our deep neural network, we use a simple embedding of bytes in an 8-dimensional space, in which each bit of the byte corresponds to a coordinate of the vector (Step 2). A bit equal to 1 means that the corresponding coordinate is set to 1/16, whereas a bit value of 0 corresponds to a coordinate equal to -1/16. For example, 10010001 is embedded as the vector (1/16, -1/16, -1/16, 1/16, -1/16, -1/16, -1/16, 1/16). Other ways to perform embeddings, such as ones that are trained along with the neural network, are possible.

The MalConv architecture ...

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Publisher Resources

ISBN: 9781789614671Supplemental Content