Become a master at penetration testing using machine learning with Python

Key Features
Identify ambiguities and breach intelligent security systems
Perform unique cyber attacks to breach robust systems
Learn to leverage machine learning algorithms

Book Description
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.

What you will learn
Take an in-depth look at machine learning
Get to know natural language processing (NLP)
Understand malware feature engineering
Build generative adversarial networks using Python libraries
Work on threat hunting with machine learning and the ELK stack
Explore the best practices for machine learning

Who this book is for
This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

Table of Contents
Introduction to Machine Learning in Pentesting
Phishing Domain Detection
Malware Detection with API Calls and PE Headers
Malware Detection with Deep Learning
Botnet Detection with Machine Learning
Machine Learning in Anomaly Detection Systems
Detecting Advanced Persistent Threats
Evading Intrusion Detection Systems with Adversarial Machine Learning
Bypass machine learning malware Detectors
Best Practices for Machine Learning and Feature Engineering
Assessments