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Time Series Analysis and Ensemble Modeling

In this chapter, we will study two important concepts of machine learning: time series analysis and ensemble learning. These are important concepts in the field of machine learning.

We use these concepts to detect anomalies within a system. We analyze historic data and compare it with the current data to detect deviations from normal activities.

The topics that will be covered in this chapter are the following:

Time series and its different classes
Time series decomposition
Analysis of time series in cybersecurity
Prediction of DDoS attack
Ensemble learning methods and voting ensemble methods to detect cyber attacks

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Hands-On Machine Learning for Algorithmic Trading

€49.99

Hands-On Artificial Intelligence for Cybersecurity

€36.99

Hands-On Machine Learning for Cybersecurity

€36.99

Total € 123.97

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Endoplasmic Reticulum May 16, 2019

Still reading it

Amazon Verified review

Navya Nov 16, 2019

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.

countermode Feb 05, 2020

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.

Alvaro Feb 18, 2020

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.

Luciano Montenegro Oct 01, 2023

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.

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

What do you get with Print?

Hands-On Machine Learning for Cybersecurity

Time Series Analysis and Ensemble Modeling

What is a time series?

Classes of time series models

Stochastic time series model

Time series decomposition

Level

Use cases for time series

Signal processing

Time series analysis in cybersecurity

Time series trends and seasonal spikes

Predicting DDoS attacks

ARMA

ARIMA

Ensemble learning methods

Types of ensembling

Voting ensemble method to detect cyber attacks

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