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vic hargrave
97 followers -
I live to code and code to live, with frequent shots of coffee along the way.
I live to code and code to live, with frequent shots of coffee along the way.

97 followers
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Kubernetes gives you the capabilities to easily spin up clusters to run your Docker application containers. As such, Kubernetes is an ideal environment for running Elasticsearch clusters in the cloud.

I’ve been working with Elasticsearch on Kubernetes quite a bit lately so I thought I’d share with you how you can deploy your own Elasticsearch cluster with all the latest bells and whistles.

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Network packet capture and analysis are commonly done with tools like tcpdump, snort, and Wireshark. These tools provide the capability to capture packets live from networks and store the captures in PCAP files for later analysis. A much better way to store packets is to index them in Elasticsearch where you can easily search for packets based on any combination of packet fields.

Pyshark is a module that provides a wrapper API to tshark – the command line version of Wireshark – with which you can build packet capture applications that take advantage of all the Wireshark protocol dissectors. With Pyshark and the Elasticsearch Python client library you can easily create a simple packet capture application in Python that can index packets in Elasticsearch.

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It’s been awhile since Kibana 4 was released, so I figured it was about time I updated my OSSEC Log Management Console to use the latest and greatest Kibana. The look and feel of Kibana has changed quite a bit, with a new data discovery mode that let’s you browse your data quickly before you create any visualizations. The visualization panels are fluidly moveable to any position, query results are displayed very rapidly and you can even embed your dashboards into static web pages with the dashboard export feature.

In this article I’ll go over how to create a security event dashboard with KIbana 4. I’ll forgo discussing the details on setting up Elasticsearch and Logstash since they have been covered in my previous OSSEC log management and logstash blogs. Read those first to get an idea of how the system described here parses OSSEC alert logs and indexes them with Elasticsearch.

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Benjamin Franklin once wrote “…in this world nothing can be said to be certain, except death and taxes”. In this computerized world of ours, I would add having to backup your data to free up disk space to that list of eventualities.

For Elasticsearch users, backups are done using the Elasticsearch snapshot facility. In this article I’ll go through the design of an Elasticsearch backup system that you can use to create snapshots of your cluster’s indices and documents.

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The ELK stack (Elasticsearch-Logstash-Kibana) provides a cost effective alternative to commercial SIEMs for ingesting and managing OSSEC alert logs. Previously I wrote a blog – OSSEC Log Management with Elasticsearch – that discusses the design of an ELK based log system.

Since then some readers have asked for and suggested ways to parse additional fields from the OSSEC alert log stream. For example, the IP addresses of systems that cause certain security events is buried down in the Details field. So I have created a Logstash configuration file that does just that.

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The first article in this two part series focused on developing Elasticsearch clients with Perl. Elasticsearch also has an excellent Python library which lets you search for and analyze your data with one of the many mathematics and machine learning libraries available for Python.

In this article I’ll cover how to create an Elasticsearch client using Python that has the same capabilities as the Perl client from the part 1 article.

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Since creating a log management system for the OSSEC HIDS with Elasticsearch, I have been busy applying this useful search technology in other projects. Elasticsearch is a marvelous system for ingesting streaming data that gets indexed on the fly and quickly searching your data.

The Elasticsearch community provides client libraries that expose their search API in several popular languages, including Perl and Python. This article is the first of a two part series where I show you how to write an Elasticsearch search client application in both of these languages, starting with Perl.

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Among the many useful features of OSSEC is its capability to send alerts to any system that can consume syslog data. This makes it easy to combine OSSEC with a number of 3rd party SIEMs to store, search and visualize security events. Splunk for OSSEC is one such system that works on top of the Splunk platform.

Splunk can be expensive though, particularly if you collect a lot of log data. So I’ve been working on a solution for collecting OSSEC security alerts based on Elasticsearch that provides a cost effective alternative to Splunk.

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I've got to have one of these.

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