Your log analysis solution works through millions of lines of logs, which makes implementing a machine learning solution essential. Organizations are turning to machine learning log alerts as a replacement or enhancement of their traditional threshold alerts. As service uptime becomes a key differentiator, threshold alerts are only as good as your ability to foresee an issue.
The first time I created a cloud compute instance, then still called a “Cloud VM”, was an almost transcendent moment. It was like magic. I was at my first organization which had adopted the cloud, in my first DevOps position, and I immediately knew that the world had changed.
Before we dive into the gap in cloud network security, let’s take a step back. If you’ve been in Operations for a while, you might remember how it used to be. “Network” was a team. When you needed to open a port on the network, you had to provide an exhaustive definition of the change, explaining what port you needed, what external addresses should be able to reach it, and where it should be routed to internally.
If our end users end up too long for a query to return results due to Elasticsearch query performance issues, it can often lead to frustration. Slow queries can affect the search performance of an ecommerce site or a Business Intelligence dashboard – either way, this could lead to negative business consequences. So it’s important to know how to monitor the speed of search queries, diagnose and debug to improve search performance.
This post will help you write effective Snort Rules to materially improve your security posture. We’ll begin with a breakdown of how a Rule is constructed and then explore best practices with examples in order to capture as many malicious activities as possible while using as few rules as possible. Snort is an open-source network intrusion detection system (NIDS) that provides real-time packet analysis and is part of the Coralogix STA solution.
As the leading log management and analytics platform, we’re proud to announce that we’ve been recognized in the Gartner “2020 Cool Vendors in Performance Analysis” report by Padraig Byrne. Gartner, Inc. is the world’s leading information technology research and advisory company. They offer world-class, objective insights on a wide variety of IT solutions.
We at Coralogix, believe that cloud security is not a “nice-to-have” feature – something that only large organizations can benefit from or are entitled to have. We believe it’s a basic need that should be solved for organizations of any shape and size. This is why we built the Coralogix Security Traffic Analyzer (STA) tool for packet sniffing and automated analysis. Today we’re announcing several new features to our security product you’ll find interesting.
In the previous article, we have created the Continuous Integration (CI) pipeline for a simple Java application. It is now time to start working on the Continuous Deployment (CD) pipeline that will take the Java application and deploy it to AWS. To build the CD pipeline, we will extend the existing AWS Jenkins pipeline. If you have missed the previous article on building a CI pipeline for a Java application using Jenkins, make sure you read that first before continuing.
Log scaling is something that should be top of mind for organizations seeking to future-proof their logging solutions. Logging requirements will grow through use, particularly if not maintained or utilized effectively. There are barriers to successful log scaling, and in this post we’ll be discussing storage volume problems; increased load on the ELK stack, the amount of ‘noise’ generated by a growing ELK stack, and the pains of managing burgeoning clusters of nodes.
Monitoring Jenkins is a serious challenge. Logging is often overlooked, but it provides a wealth of information about the health of your Jenkins instance. The following are some approaches to generating informative logging to these issues, that can help to monitor and provide suitable explanations of where the problems lie; even identifying what the possible solutions are.
