更新时间：2021-07-02 13:48:46
coverpage
Title Page
Copyright and Credits
Machine Learning with the Elastic Stack
Dedication
About Packt
Why subscribe?
Packt.com
Contributors
About the authors
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Machine Learning for IT
Overcoming the historical challenges
The plethora of data
The advent of automated anomaly detection
Theory of operation
Defining unusual
Learning normal unsupervised
Probability models
Learning the models
De-trending
Scoring of unusualness
Operationalization
Jobs
ML nodes
Bucketization
The datafeed
Supporting indices
.ml-state
.ml-notifications
.ml-anomalies-*
The orchestration
Summary
Installing the Elastic Stack with Machine Learning
Installing the Elastic Stack
Downloading the software
Installing Elasticsearch
Installing Kibana
Enabling Platinum features
A guided tour of Elastic ML features
Getting data for analysis
ML job types in Kibana
Data Visualizer
The Single metric job
Multi-metric job
Population job
Advanced job
Controlling ML via the API
Event Change Detection
How to understand the normal rate of occurrence
Exploring count functions
Summarized counts
Splitting the counts
Other counting functions
Non-zero count
Distinct count
Counting in population analysis
Detecting things that rarely occur
Counting message-based logs via categorization
Types of messages that can be categorized by ML
The categorization process
Counting the categories
Putting it all together
When not to use categorization
IT Operational Analytics and Root Cause Analysis
Holistic application visibility
The importance and limitations of KPIs
Beyond the KPIs
Data organization
Effective data segmentation
Custom queries for ML jobs
Data enrichment on ingest
Leveraging the contextual information
Analysis splits
Statistical influencers
Bringing it all together for root cause analysis
Outage background
Visual correlation and shared influencers
Security Analytics with Elastic Machine Learning
Security in the field
The volume and variety of data
The geometry of an attack
Threat hunting architecture
Layer-based ingestion
Threat intelligence
Investigation analytics
Assessment of compromise
