Machine Learning for Cybersecurity Cookbook

More Information
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
About

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.

Features
  • Manage data of varying complexity to protect your system using the Python ecosystem
  • Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineering
  • Automate your daily workflow by addressing various security challenges using the recipes covered in the book
Page Count 346
Course Length 10 hours 22 minutes
ISBN 9781789614671
Date Of Publication 25 Nov 2019

Authors

Emmanuel Tsukerman

Emmanuel Tsukerman graduated from Stanford University and obtained his Ph.D. from UC Berkeley. In 2017, Dr. Tsukerman's anti-ransomware product was listed in the Top 10 ransomware products of 2018 by PC Magazine. In 2018, he designed an ML-based, instant-verdict malware detection system for Palo Alto Networks' WildFire service of over 30,000 customers. In 2019, Dr. Tsukerman launched the first cybersecurity data science course.