Emmanuel Tsukerman - Machine Learning for Cybersecurity Cookbook

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Emmanuel Tsukerman graduated from Stanford University and obtained his Ph.D. from UC Berkeley. In 2017, Dr. Tsukerman's anti-ransomware product was listed in the Top 10 ransomware products of 2018 by PC Magazine. In 2018, he designed an ML-based, instant-verdict malware detection system for Palo Alto Networks' WildFire service of over 30,000 customers. In 2019, Dr. Tsukerman launched the first cybersecurity data science course.

Alexander Osipenko graduated cum laude with a degree in computational chemistry. He worked in the oil and gas industry for 4 years, working with real-time data streaming and large network data. Then, he moved to the FinTech industry and cybersecurity. He is currently a machine learning leading expert in the company, utilizing the full potential of AI for intrusion detection and insider threat detection.

Yasser Ali is a cybersecurity consultant at Thales, in the Middle East. He has extensive experience in providing consultancy and advisory services to enterprises on implementing cybersecurity best practices, critical infrastructure protection, red teaming, penetration testing, and vulnerability assessment, managing bug bounty programs, and web and mobile application security assessment. He is also an advocate speaker and participant in information security industry discussions, panels, committees, and conferences, and is a specialized trainer, featuring regularly on different media platforms around the world.

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Cyber threats today are one of the key problems every organization faces. This book uses various Python libraries, such as TensorFlow, Keras, scikit-learn, and others, to uncover common and not-so-common challenges faced by cybersecurity researchers.

The book will help readers to implement intelligent solutions to existing cybersecurity challenges and build cutting edge implementations that cater to increasingly complex organizational needs. By the end of this book, you will be able to build and use machine learning ( ML ) algorithms to curb cybersecurity threats using a recipe-based approach.

This book is for cybersecurity professionals and security researchers who want to take their skills to the next level by implementing machine learning algorithms and techniques to upskill computer security. This recipe-based book will also appeal to data scientists and machine learning developers who are now looking to bring in smart techniques into the cybersecurity domain. Having a working knowledge of Python and being familiar with the basics of cybersecurity fundamentals will be required.

, Machine Learning for Cybersecurity, covers the fundamental techniques of machine learning for cybersecurity.

, Machine Learning-Based Malware Detection, shows how to perform static and dynamic analysis on samples. You will also learn how to tackle important machine learning challenges that occur in the domain of cybersecurity, such as class imbalance and false positive rate (FPR) constraints.

, Advanced Malware Detection, covers more advanced concepts for malware analysis. We will also discuss how to approach obfuscated and packed malware, how to scale up the collection of N-gram features, and how to use deep learning to detect and even create malware.

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