▶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
▶Key Features
⦁ Identify ambiguities and breach intelligent security systems
⦁ Perform unique cyber attacks to breach robust systems
⦁ Learn to leverage machine learning algorithms
▶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.
▶What this book covers
⦁ Chapter 1, Introduction to Machine Learning in Pentesting, introduces reader to the fundamental concepts of the different machine learning models and algorithms, in addition to learning how to evaluate them. It then shows us how to prepare a machine learning development environment using many data science Python libraries.
⦁ Chapter 2, Phishing Domain Detection, guides us on how to build machine learning models to detect phishing emails and spam attempts using different algorithms and natural language processing (NLP).
⦁ Chapter 3, Malware Detection with API Calls and PE Headers, explains the different approaches to analyzing malware and malicious software, and later introduces us to some different techniques for building a machine learning-based malware detector.
⦁ Chapter 4, Malware Detection with Deep Learning, extends what we learned in the previous chapter to explore how to build artificial neural networks and deep learning to detect malware.
⦁ Chapter 5, Botnet Detection with Machine Learning, demonstrates how to build a botnet detector using the previously discussed techniques and publicly available botnet traffic datasets.
⦁ Chapter 6, Machine Learning in Anomaly Detection Systems, introduces us to the most important terminologies in anomaly detection and guides us to build machine learning anomaly detection systems.
⦁ Chapter 7, Detecting Advanced Persistent Threats, shows us how to build a fully working real-world threat hunting platform using the ELK stack, which is already loaded by machine learning capabilities.
⦁ Chapter 8, Evading Intrusion Detection Systems with Adversarial Machine Learning, demonstrates how to bypass machine learning systems using adversarial learning and studies some real-world cases, including bypassing next-generation intrusion detection systems.
⦁ Chapter 9, Bypass Machine Learning Malware Detectors, teaches us how to bypass machine learning-based malware detectors with adversarial learning and generative adversarial networks.
⦁ Chapter 10, Best Practices for Machine Learning and Feature Engineering, explores different feature engineering techniques, in addition to introducing readers to machine learning best practices to build reliable systems.
