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Hands-On Machine Learning for Cybersecurity

You're reading from  Hands-On Machine Learning for Cybersecurity

Product type Book
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Pages 318 pages
Edition 1st Edition
Languages
Authors (2):
Soma Halder Soma Halder
Profile icon Soma Halder
Sinan Ozdemir Sinan Ozdemir
Profile icon Sinan Ozdemir
View More author details
Toc

Table of Contents (13) Chapters close

Preface 1. Basics of Machine Learning in Cybersecurity 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

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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