The human mind is a fascinating entity. The power of our subconscious and unconscious mind is incredible. What makes this power real is our ability to continuously self-learn and adapt quickly. This amazing gift of nature can calculate billions of tasks before you even realize what it does. For decades, scientists have been trying to build machines that are able to do simultaneous tasks like the human mind does—in other words, systems that are able to perform a huge number of tasks efficiently and at incredible speeds. A subfield of machine learning called Deep Learning (DL) arose to help us build algorithms that work like the human mind and are inspired by its structure. Information security professionals are also intrigued by such techniques, as they have provided promising results in defending against major cyber threats and attacks...
Mastering Machine Learning for Penetration Testing
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Mastering Machine Learning for Penetration Testing
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Overview of this book
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.
Table of Contents (13 chapters)
Preface
Free Chapter
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
Bypassing Machine Learning Malware Detectors
Best Practices for Machine Learning and Feature Engineering
Assessments
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