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Getting Started with Elastic Stack 8.0

You're reading from   Getting Started with Elastic Stack 8.0 Run powerful and scalable data platforms to search, observe, and secure your organization

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781800569492
Length 474 pages
Edition 1st Edition
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Author (1):
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Asjad Athick Asjad Athick
Author Profile Icon Asjad Athick
Asjad Athick
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Core Components
2. Chapter 1: Introduction to the Elastic Stack FREE CHAPTER 3. Chapter 2: Installing and Running the Elastic Stack 4. Section 2: Working with the Elastic Stack
5. Chapter 3: Indexing and Searching for Data 6. Chapter 4: Leveraging Insights and Managing Data on Elasticsearch 7. Chapter 5: Running Machine Learning Jobs on Elasticsearch 8. Chapter 6: Collecting and Shipping Data with Beats 9. Chapter 7: Using Logstash to Extract, Transform, and Load Data 10. Chapter 8: Interacting with Your Data on Kibana 11. Chapter 9: Managing Data Onboarding with Elastic Agent 12. Section 3: Building Solutions with the Elastic Stack
13. Chapter 10: Building Search Experiences Using the Elastic Stack 14. Chapter 11: Observing Applications and Infrastructure Using the Elastic Stack 15. Chapter 12: Security Threat Detection and Response Using the Elastic Stack 16. Chapter 13: Architecting Workloads on the Elastic Stack 17. Other Books You May Enjoy

Looking for anomalies in time series data

Given the logs in the webapp index, there is some concern that there was some potentially undesired activity happening on the application. This could be completely benign or have malicious consequences. This section will look at how a series of machine learning jobs can be implemented to better understand and analyze the activity in the logs.

Looking for anomalous event rates in application logs

We will use a single-metric machine learning job to build a baseline for the number of log events generated by the application during normal operation.

Follow these steps to configure the job:

  1. Open the machine learning app from the navigation menu and click on the Anomaly Detection tab.
  2. Click on Create job and select the webapp data view. You could optionally use a saved search here with predefined filters applied to narrow down the data used for the job.
  3. Create a single-metric job as we're only interested in the event...
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