Mastering Machine Learning for Penetration Testing

Mastering Machine Learning for Penetration Testing equips you with the specialized knowledge needed to breach self-learning security systems effectively and ethically. By combining machine learning fundamentals with penetration testing techniques, this book empowers you to understand and outsmart intelligent security systems.

What this Book will help me do

• Gain a comprehensive understanding of machine learning principles and their application in security systems.
• Leverage Python libraries to build and analyze generative adversarial networks for penetration testing.
• Explore new tactics for bypassing anomaly detection systems using adversarial machine learning.
• Acquire practical skills in detecting vulnerabilities in systems protected by deep learning algorithms.
• Develop advanced strategies for penetration testing in intelligent systems through hands-on case studies.

Author(s)

Chiheb Chebbi is an experienced cybersecurity professional specializing in penetration testing and advanced security analysis. With years of hands-on experience in ethical hacking and a deep understanding of machine learning applications, Chiheb brings a practical and insightful approach to teaching. His work focuses on empowering security professionals through knowledge and proven techniques.

Who is it for?

This book is designed for professionals in penetration testing, cybersecurity analysts, and anyone keen on improving their knowledge of machine learning's role in security. There is no need for prior machine learning experience, though readers should be familiar with Python programming and basic cybersecurity concepts. If you are looking to advance your offensive security techniques, this book is ideal for you.