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Machine Learning for Cybersecurity Cookbook

Machine Learning for Cybersecurity Cookbook

By : Emmanuel Tsukerman
3 (2)
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Machine Learning for Cybersecurity Cookbook

Machine Learning for Cybersecurity Cookbook

3 (2)
By: Emmanuel Tsukerman

Overview of this book

Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach.
Table of Contents (11 chapters)
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Voice impersonation

Using the new technology of voice style transfer via neural networks, it is becoming easier and easier to convincingly impersonate a target's voice. In this section, we show you how to use deep learning to have a recording of a target saying whatever you want them to say, for example, to have a target's voice used for social engineering purposes or, a more playful example, using Obama's voice to sing Beyoncé songs. We selected the architecture in mazzzystar/randomCNN-voice-transfer that allows for fast results with high quality. In particular, there is no need to pre-train the model on a large dataset of recorded audio.

In the accompanying code for this book, you will find two versions of the voice transfer neural network code, one for GPU and one for CPU. We describe here the one for CPU, though the one for GPU is very similar...

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