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Mastering Machine Learning for Penetration Testing

You're reading from   Mastering Machine Learning for Penetration Testing Develop an extensive skill set to break self-learning systems using Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997409
Length 276 pages
Edition 1st Edition
Languages
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Author (1):
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Chiheb Chebbi Chiheb Chebbi
Author Profile Icon Chiheb Chebbi
Chiheb Chebbi
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Machine Learning in Pentesting 2. Phishing Domain Detection FREE CHAPTER 3. Malware Detection with API Calls and PE Headers 4. Malware Detection with Deep Learning 5. Botnet Detection with Machine Learning 6. Machine Learning in Anomaly Detection Systems 7. Detecting Advanced Persistent Threats 8. Evading Intrusion Detection Systems 9. Bypassing Machine Learning Malware Detectors 10. Best Practices for Machine Learning and Feature Engineering 11. Assessments 12. Other Books You May Enjoy

Implementing neural networks in Python

Classic computer programs are great when it comes to compute operations based on a sequence of instructions and arithmetic, but they face difficulties and challenges in many other cases; for example, handwriting-recognition. As a warm up, let's build a handwritten digit recognizer to take the opportunity to install the Python libraries needed in the next sections and learn how to build and implement our first neural network in Python. To train the model, we need to feed it with data. In our implementation, we are going to use the MNIST dataset:

First, let's install the keras library using the pip install command, as shown here:

# pip install keras

Then, install TensorFlow (tensorflow) using the following command:

# pip install tensorflow

And finally, install np_utils:

# pip install np_utils

Open the Python command-line interface...

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