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

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Chiheb Chebbi is an InfoSec enthusiast who has experience in various aspects of information security, focusing on the investigation of advanced cyber attacks and researching cyber espionage and APT attacks. Chiheb is currently pursuing an engineering degree in computer science at TEK-UP university in Tunisia.

His core interests are infrastructure penetration testing, deep learning, and malware analysis. In 2016, he was included in the Alibaba Security Research Center Hall Of Fame. His talk proposals were accepted by DeepSec 2017, Blackhat Europe 2016, and many world-class information security conferences.

Aditya Mukherjee is a proficient information security professional, cybersecurity speaker, entrepreneur, cybercrime investigator, and columnist.

He has 10+ years of experience in different leadership roles across information security domains with various reputed organizations, specializing in the implementation of cybersecurity solutions, cyber transformation projects, and solving problems associated with security architecture, framework, and policies.

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Currently, machine learning techniques are some of the hottest trends in information technology. They impact on every aspect of our lives, and they affect every industry and field. Machine learning is a cyber weapon for information security professionals. In this book, you will not only explore the fundamentals of machine learning techniques, but will also learn the secrets to building a fully functional machine learning security system; we will not stop at building defensive layers. We will explore how to attack machine learning models with adversarial learning. Mastering Machine Learning for Penetration Testing will provide educational as well as practical value.

Mastering Machine Learning for Penetration Testing is for pen testers and security professionals who are interested in learning techniques for breaking an intelligent security system. A basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

, 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.

, 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).

, 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.

, 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.

, Botnet Detection with Machine Learning, demonstrates how to build a botnet detector using the previously discussed techniques and publicly available botnet traffic datasets.

, Machine Learning in Anomaly Detection Systems