Malware Data Science: Attack Detection and Attribution

As a data scientist and machine learning practitioner I was disappointed in this book. Admittedly the authors faced the very difficult task of trying to communicate the relationship of two very deep and technical subjects: malware analysis and machine learning. The result, unfortunately, is a work that is only the barest introduction to both.The first half of the book dips a toe in the ocean of malware static analysis, dynamic analysis, and reverse engineering. Not knowing anything about malware, this section of the book did indeed spark my curiosity and supply me with strong motivation to continue my studies in malware analysis with other, more comprehensive resources.The second half of the book is a shallow overview of basic, elementary topics in machine learning and common tools used by practitioners. This overview is accompanied by proof-of-concept applications to malware detection. The machine learning overview will not supply any new knowledge to anyone who has any practical experience in machine learning and for a complete beginner it is insufficient even as a first course.In summary, this is not a bad book as the authors are clearly technically proficient, good writers, and passionate about their area of expertise. Nonetheless, be aware that if you are not already an expert in malware AND machine learning you will need to follow up this short read with much more detailed sources. If you are already proficient in both subjects than you will already understand how to apply machine learning to malware analysis to the level of basic examples found in this book.

I loved this book, it gives you a quick intro to disassemble and "reverse engineering", and it states that if you want to go deeper you can read Practical Malware analysis, which I think could be the next level of this book, from there it's just a roller coaster of binary data analysis, data visualization and coding. Joshua and Hillary made a good job at keeping the code readable, while teaching some key concepts on ML and deep learning, data visualization and analysis. In the end, the book not only feeds you with plenty of information, but also is able to give you new ideas.

This book is a straightforward and useful guide to malware analysis, including machine learning and deep learning methods of IDS. The website includes a downloadable VM for execution in VirtualBox on a student's laptop. It's a little light in some areas, but would be great in combination with another book that went into more details about reverse engineering, disassembly, static and dynamic analysis, etc.

The book arrived in perfect condition, not a single blemish anywhere. As a collector of books from No Starch Press, I value this text and the material discussed within is invaluable. I recommend this book for all data scientists, infosec noobs and vets, hackers of all skill levels, ANYONE with an interest in cybersecurity or hacking.

Wow, this is one amazing introduction. I’m usually reading other bug hunting books, then I came upon this book. I kind of shined away from machine learning, however this book provided me with surprises, pleasent ones. A very 8nteresting book.

Malware Data Science book is one of the best book in the industry to start with, to figure out this complex field more deeply. I recommend all professionals to read it, and try to make use of its practical applications in ML/DL/AI.

Nothing to dislike and exactly the information I needed, although it's taking a good bit of time to read and absorb. ;)

The intersection of data science and infosec is smaller than should be. The authors have done a great job explaining complex topics clearly, and with good code.

not every page is the same

Muy fan de nostach siempre contenido de gran calidad

Muy interesante, con código de ejemplo y bastante bien explicado.

Muy bueno!

Hands on book