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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

Using Windows event logs to detect network anomalies

We will use Windows event logs to detect lateral movement in the first pass of detecting network anomalies. We will use Windows Active Directory logs for the purpose of the experiment. Active Directory is a Microsoft product that provides a directory service for network domains. Active Directory services include a wide range of directory-based identity-related services.

Active Directory stores all sorts of authorization and authentication logs using lightweight directory access protocol (LDAP). Active Directory logs a host of processes such as log-on events. In other words, when someone logs on to a computer and lockout events, that is, when someone enters wrong passwords and is unable to login. The following diagram shows the Active Directory logs along with the different processes:

We will discuss each of these types so that...

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