Machine Learning for Cybersecurity Cookbook: Over 80 recipes on how to implement machine learning algorithms for building security systems using Python

Machine Learning for Cybersecurity Cookbook: Over 80 recipes on how to implement machine learning algorithms for building security systems using Python
Machine Learning for Cybersecurity Cookbook: Over 80 recipes on how to implement machine learning algorithms for building security systems using Python by Emmanuel Tsukerman
English | 2019 | ISBN: 1789614671 | 346 Pages | True PDF, EPUB | 1052 MB

Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection
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.
What you will learn

  • Learn how to build malware classifiers to detect suspicious activities
  • Apply ML to generate custom malware to pentest your security
  • Use ML algorithms with complex datasets to implement cybersecurity concepts
  • Create neural networks to identify fake videos and images
  • Secure your organization from one of the most popular threats – insider threats
  • Defend against zero-day threats by constructing an anomaly detection system
  • Detect web vulnerabilities effectively by combining Metasploit and ML
  • Understand how to train a model without exposing the training data