Data Mining and Machine Learning in Cybersecurity

by Sumeet Dua, Xian Du

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need. From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges―detailing cutting-edge machine learning and data mining techniques. It This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.

Genres: Computer Science, Technology, Mathematics

256 pages, Hardcover

First published April 25, 2011

Reviews

[Takedown · Rating 3 out of 5]

Decent book if you need short overview of machine learning algorithms and their application to security. It also contains all relevant publications in references. Although it is purely theoretical.

[Moshe Zioni · Rating 1 out of 5]

I'm almost regret of ever starting to read this book.

This is a very introductory way into Machine Learning oriented towards Cyber Security needs. I've started this book around a year and a half ago and I have this bad habit of not letting down a book, no matter the time it takes me to read.

Now, I consider myself somewhat savvy in mathematics, statistics and cybersecurity - that is to say the statistical/mathematical ideas of this book are not something to be afraid of - if you have some knowledge or training in statistics, differential equations and linear algebra - you should be absolutely fine - if not - you can skip the math and go to its context (you won't be able to implement it by yourself or choose a proper model, but just for the basics). Before acknowledging that - I was literally stuck many times on equations that I couldn't derive by myself - checking with a data scientist more senior colleague - he explained to me how wrong the book is. Frustrated more about the time it demanded from me than the actual fault - still, the principle of finishing this book was strong within me.

The big "but" of this book is that it is, in parts, incoherent and unreadable - it seems like a very rush publishing, the foundations are not set and the terminology is often mixed and unclear (I spotted several (mis)uses of terms and above all they weren't introduced anywhere in the book before nor after the single mention) - and that's very sad because there are many in the field that would love to find such a book with clear definitions and overview.

It does cover history quite well - if you can ignore the errors, math and sometimes even plain mistakes - you will be fine. If not - move along.

[Balmung · Rating 3 out of 5]

I've found this book full of citations to several papers and nothing more. It could be useful for a first insight in the area