Security monitoring log management-describe logstash,kibana,elastic slidshare

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Implement your own Log management Develop your tools for Monitoring Understand Elastic implement Elastic, Kibana & Logstash Modern Log parsing

Données & analyses

Security Event Monitoring,Log
Management Describe:
“LogStash,Elastic & Kibana"
Present & Gathered by:
Reza Adineh
Cyber Security Specialist
SOC Expert
Forensic Researcher
Contact me:  
https://ir.linkedin.com/in/rezaadineh
 
Feb-2018

Heart of ELK stack: Elasticsearch
Based on Apache Lucene
Shay Banon, Compass to Elasticsearch, released in 2010
In 2012 Elastic was founded in Amsterdam
RESTful search & Analytics engine

Plugin Ecosystem:
Rich Integration & Processing
200+ plugins
Extensible framework to easily build your own plugin
Logstash Plugins Maintainer Program

Need for log analysis
Lets understand why do we need log analysis ?

Problems with log analysis
Lets understand what problems occurred with log analysis ?

Lets now understand what exactly is ELK Stack.

Visualizing logs using ElasticSearch, Logstach & Kibana &
saving millions !

Keep a deeper look at Logs & how implement
ElasticSearch, Logstach & Kibana
Logs & Log structures:

A log is human readable …
A human readable, machine parsable representation of an
event.

Regex ?!
How to parse logs ?
OR Indexing & Labeling

Thinking open source :
Logstash
Graylog
Logalyse
Scribe
Hadooooop
Did you like it ?
Lets look at Logstash …

Already have central Rsyslog/SyslogNg Server ?

Also you can use it as Central Syslog Server
It is too good for Appliances

Use matching input & outputs to Sendﬁle contents to
another log stash for processing.

Further reading on :
logstash.net
logstashbook.com
Juju charms.com/charms/precise/logstash-indexer
Logstash puppet module(github/electrical)

Any question ?
Contact me:  
https://ir.linkedin.com/in/
rezaadineh

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This project demonstrates a machine learning approach to detecting credit card fraud using advanced algorithms and techniques. The project utilizes a dataset containing various features such as transaction amount, merchant location, time of transaction, and others to build a predictive model. The presentation covers data preprocessing steps, feature engineering techniques, and the selection of machine learning algorithms such as logistic regression or random forest. It also discusses model evaluation metrics and the importance of fraud detection in financial institutions for safeguarding against fraudulent activities. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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