Mastering Machine Learning for Penetration Testing: Develop an extensive skill set to break self-learning systems using Python

by Chiheb Chebbi

Become a master at penetration testing using machine learning with Python 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. 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.

276 pages, Paperback

Published June 27, 2018

Reviews

[Carter · Rating 3 out of 5]

At this point, I do not particularly advocate, this approach- perhaps this will change with changes like neurosymbolic efforts, combining DNN with symbolic capabilities. Right now, the consensus, is it is hard to latch onto patterns in code, that make malware easy to recognize. (This is a review for the Japanese edition.)