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
Chiheb Chebbi is an InfoSec enthusiast who has experience in various aspects of information security, focusing on the investigation of advanced cyber attacks and researching cyber espionage and APT attacks. Chiheb is currently pursuing an engineering degree in computer science at TEK-UP university in Tunisia.
His core interests are infrastructure penetration testing, deep learning, and malware analysis. In 2016, he was included in the Alibaba Security Research Center Hall Of Fame. His talk proposals were accepted by DeepSec 2017, Blackhat Europe 2016, and many world-class information security conferences.
