We assume that the readers of this book are familiar with basic information security concepts and Python programming. Some of the demonstrations in this book require more practice and online research to delve into the concepts discussed.

Always check the GitHub repository of this book to check for updated code if you encounter any bugs, typos, or errors.

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packtpub.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Mastering-Machine-Learning-for-Penetration-Testing. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it from https://www.packtpub.com/sites/default/files/downloads/MasteringMachineLearningforPenetrationTesting_ColorImages.pdf.

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "First, check your Python version with the python --version command."

A block of code is set as follows:

from keras import [what_to_use]
from keras.models import Sequential
from keras.layers import Dense

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

model = Sequential()
# N = number of neurons
# V = number of variable
model.add(Dense(N, input_dim=V, activation='relu'))
# S = number of neurons in the 2nd layer
model.add(Dense(S, activation='relu'))
model.add(Dense(1, activation='sigmoid')) # 1 output

Any command-line input or output is written as follows:

>>> import tensorflow as tf
>>> Message = tf.constant("Hello, world!")
>>> sess = tf.Session()
>>> print(sess.run(Message))

Bold: Indicates a new term, an important word, or words that you see onscreen.