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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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Toc

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

What is a time series?

A time series is defined as an array of data points that is arranged with respect to time. The data points are indicative of an activity that takes place at a time interval. One popular example is the total number of stocks that were traded at a certain time interval with other details like stock prices and their respective trading information at each second. Unlike a continuous time variable, these time series data points have a discrete value at different points of time. Hence, these are often referred to as discrete data variables. Time series data can be gathered over any minimum or maximum amount of time. There is no upper or lower bound to the period over which data is collected.

Time series data has the following:

  • Specific instances of time forming the timestamp
  • A start timestamp and an end timestamp
  • The total elapsed time for the instance

The...

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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