Machine Learning for Cybersecurity Cookbook: Over 80 recipes on how to implement machine learning algorithms for building security systems using Python
Authors: Emmanuel Tsukerman
ISBN-10: 1789614678
ISBN-13: 9781789614671
Publication Date 出版日期: 2019-11-25
Print Length 页数: 346 pages
Book Description
By finelybook
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
Contents
Preface
Chapter 1: Machine Learning for Cybersecurity
Chapter 2: Machine Learning-Based Malware Detection
Chapter 3: Advanced Malware Detection
Chapter 4: Machine Learning for Social Engineering
Chapter 5: Penetration Testing Using Machine Learning
Chapter 6: Automatic Intrusion Detection
Chapter 7: Securing and Attacking Data with Machine Learning
Chapter 8: Secure and Private Al
Appendix
Other Books You May Enjoy
Index
