Packt Publishing (July 10, 2018) | ISBN: 1788997409 | 497 pages | EPUB
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
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
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
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Mastering Machine Learning for Penetration Testing - Develop extensive skillsets.epub
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