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Hands-On Machine Learning for Cybersecurity
Hands-On Machine Learning for Cybersecurity

Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your machines intelligent using the Python ecosystem

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Profile Icon Halder Profile Icon Sinan Ozdemir
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€26.98 €29.99
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7 (6 Ratings)
eBook Dec 2018 318 pages 1st Edition
eBook
€26.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Halder Profile Icon Sinan Ozdemir
Arrow right icon
€26.98 €29.99
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7 (6 Ratings)
eBook Dec 2018 318 pages 1st Edition
eBook
€26.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€26.98 €29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m

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Hands-On Machine Learning for Cybersecurity

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Key benefits

  • Learn machine learning algorithms and cybersecurity fundamentals
  • Automate your daily workflow by applying use cases to many facets of security
  • Implement smart machine learning solutions to detect various cybersecurity problems

Description

Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems

Who is this book for?

This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

What you will learn

  • Use machine learning algorithms with complex datasets to implement cybersecurity concepts
  • Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems
  • Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA
  • Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes
  • Use TensorFlow in the cybersecurity domain and implement real-world examples
  • Learn how machine learning and Python can be used in complex cyber issues

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 31, 2018
Length: 318 pages
Edition : 1st
Language : English
ISBN-13 : 9781788990967
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Languages :

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Product Details

Publication date : Dec 31, 2018
Length: 318 pages
Edition : 1st
Language : English
ISBN-13 : 9781788990967
Category :
Languages :

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Table of Contents

12 Chapters
Basics of Machine Learning in Cybersecurity Chevron down icon Chevron up icon
Time Series Analysis and Ensemble Modeling Chevron down icon Chevron up icon
Segregating Legitimate and Lousy URLs Chevron down icon Chevron up icon
Knocking Down CAPTCHAs Chevron down icon Chevron up icon
Using Data Science to Catch Email Fraud and Spam Chevron down icon Chevron up icon
Efficient Network Anomaly Detection Using k-means Chevron down icon Chevron up icon
Decision Tree and Context-Based Malicious Event Detection Chevron down icon Chevron up icon
Catching Impersonators and Hackers Red Handed Chevron down icon Chevron up icon
Changing the Game with TensorFlow Chevron down icon Chevron up icon
Financial Fraud and How Deep Learning Can Mitigate It Chevron down icon Chevron up icon
Case Studies Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7
(6 Ratings)
5 star 33.3%
4 star 0%
3 star 0%
2 star 33.3%
1 star 33.3%
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Endoplasmic Reticulum May 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Still reading it
Amazon Verified review Amazon
Navya Nov 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very well written and helped clear the concepts. However, wish this book was not restricted only to Python and even snippets of R were included.
Amazon Verified review Amazon
countermode Feb 05, 2020
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
This e-book cover topics that I was seeking suitable material for, so I was highly delighted to find a book on those topics. However, I soon discovered that the book is not at all self-explanatory in the sense that the author throws several important notions at the reader without any reference. For instance, in the chapter on time series analysis the author mentions ACF and PACF, "explaining" them with a few sentences. It took me several video lectures on YouTube (which are, of course, of diverse quality) to get the idea and some background in order to follow the author. I don't think that the concepts of ACF, PACF, auto-regression, moving average, ARMA, ARIMA etc. are "common folklore", so I wish the author had introduced them more carefully, or at least, if he had provided suitable references. What is good, at least, is that the reader gets the key words to look for so with quite some additional effort it is possible to follow the author.In the end, given the price, I am utterly disappointed; I've seen much better literature for much lower prices.
Amazon Verified review Amazon
Alvaro Feb 18, 2020
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I totally agree with and confirm the truth of the comment made by "countermode" He is fully right.The author of the book has touched upon a tremendous amount of complex concepts, something that you can tell in the Table of Contents, but then you see that she just devotes a couple of lines leaving the matter unexplained. Therefore, the reader is left to look for explanations elsewhere, like tutorials on the internet, videos etc.The reason is obvious. She has written a book of 300 pages, but the topics that she mentions would require around 1500-1800 pages.On page 46 she just breezes in about correlation time series and provides no explanation about the graphic that is shown. This is a pattern throughout the book.So, consider that you are going to have to devote about 10 times more time to learn about the topics she mentions.
Amazon Verified review Amazon
Luciano Montenegro Oct 01, 2023
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Caso, a intenção foi ser prático com o material do livro, poderia ter tido um cuidado maior nos códigos utilizados. E apresentar referências para aprofundar determinados aspectos de ML. Achei o valor muito alto para o conteúdo apresentado. Os códigos simplesmente precisam ser atualizados para versão mais atuais de python.
Amazon Verified review Amazon
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