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Mastering Machine Learning for Penetration Testing

Mastering Machine Learning for Penetration Testing

By : Chiheb Chebbi
4 (4)
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Mastering Machine Learning for Penetration Testing

Mastering Machine Learning for Penetration Testing

4 (4)
By: Chiheb Chebbi

Overview of this book

Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system.
Table of Contents (13 chapters)
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Chapter 4 – Malware Detection with Deep Learning

  1. What is the difference between MLP networks and deep learning networks?

Deep networks are already multi-layer perceptron networks but with at least three hidden layers.

  1. Why DL recently is taking off?

Because we have access to a lot more computational power and data.

  1. Why do we need to iterate multiple times through different models?

Because nobody can always find the best model or hyperparameter without iterations.

  1. What type of DL needed to translate English to French language?

Recurrent Neural Network (RNN)

  1. Why malware visualization is a good method to classify malware?

Because we can use state-of-the-art image recognition to build malware classifiers.

  1. What is the role of an activation function?

It defines the output of a given node. In other words, it converts an input signal of a node in an A-NN to an output...

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