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Hands-On Machine Learning for Cybersecurity

You're reading from   Hands-On Machine Learning for Cybersecurity Safeguard your system by making your machines intelligent using the Python ecosystem

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Length 318 pages
Edition 1st Edition
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Authors (2):
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Soma Halder Soma Halder
Author Profile Icon Soma Halder
Soma Halder
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (13) Chapters Close

Preface 1. Basics of Machine Learning in Cybersecurity 2. Time Series Analysis and Ensemble Modeling FREE CHAPTER 3. Segregating Legitimate and Lousy URLs 4. Knocking Down CAPTCHAs 5. Using Data Science to Catch Email Fraud and Spam 6. Efficient Network Anomaly Detection Using k-means 7. Decision Tree and Context-Based Malicious Event Detection 8. Catching Impersonators and Hackers Red Handed 9. Changing the Game with TensorFlow 10. Financial Fraud and How Deep Learning Can Mitigate It 11. Case Studies 12. Other Books You May Enjoy

Use cases for time series

In the Signal processing section, we will discuss the different fields where time series are utilized to extract meaningful information from very large datasets. Be it social media analysis, click stream trends, or system log generations, time series can be used to mine any data that has a similar time-sensitive approach to data collection and storage.

Signal processing

Digital signal processing uses time series analysis to identify a signal from a mixture of noise and signals. Signal processing uses various methods to perform this identification, like smoothing, correlation, convolution, and so on. Time series helps measure deviations from the stationary behaviors of signals. These drifts or deviations...

You have been reading a chapter from
Hands-On Machine Learning for Cybersecurity
Published in: Dec 2018
Publisher: Packt
ISBN-13: 9781788992282
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