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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 FREE CHAPTER 2. Time Series Analysis and Ensemble Modeling 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

Time series trends and seasonal spikes

Time series analysis can be used to detect attack attempts, like failed logins, using a time series model. Plotting login attempts identifies spikes (/) in failed logins. Such spikes are indicative of account takeover (ATO).

Time series identify another cyber security use case—data exfiltration is the process in which the unauthorized transfer of data takes place from a computer system to a malicious location. Time series can identify huge network data packets being transported out of the network. Data exfiltration could be because of either an outsider compromise or an insider threat. In a later section of the chapter, we will use ensemble learning methods to identify the source of the attack.

We will learn the details of the attack in the next section. The goal of this chapter is to be able to detect reconnaissance so that we are...

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