It was a cloudy winter morning when I had arrived at the office and found, to our horror, that a Kubernetes cluster was suffering from extremely high CPU and network usage and had become almost completely non-functional. To make things worse, restarting the nodes (the go-to DevOp solution), seemed to have absolutely no effect on the issue. Something was poisoning the network and we had to find out what it was and fast.
The evolution of Security Information and Event Management (SIEM) is deeply intertwined with cloud computing, both in terms of technological breakthroughs the cloud provided and from its inherent security challenges. With the rise of cloud computing, we no longer rely on long-lived resources. An ephemeral infrastructure obscures the identity of the components and, even if you do have the visibility it doesn’t necessarily mean you can comprehend the meaning behind the components.
The power and value that’s embedded in logs are reflected by the status and behavior of our applications and infrastructure. Many times we would like to be alerted when the application or its components show abnormal behavior. This behavior can be reflected by the application sending some logs at a higher than usual volume. Figuring out exactly what ‘higher than usual’ means, or in other words, setting the threshold value at which the alert should trigger can be a daunting task.
10 years ago, Google was facing a critical bottleneck caused by extremely prolonged C++ compilation times and needed a totally new way to solve it. Google engineers tackled the challenge by creating a new language called Go (aka Golang). The new language of Go borrows the best parts of C++, (most notably it’s performance and security features) and combines it with the speed of Python to enable Go to quickly use multiple cores while being able to implement concurrency.
Amazon ELB (Elastic Load Balancing) allows you to make your applications highly available by using health checks and intelligently distributing traffic across a number of instances. It distributes incoming application traffic across multiple targets, such as Amazon EC2 instances, containers, IP addresses, and Lambda functions. You might have heard the terms, CLB, ALB, and NLB. All of them are types of load balancers under the ELB umbrella.
So, you’ve installed Coralogix’s STA and you would like to start analyzing your traffic and getting valuable insights but you’re not sure that you’re mirroring enough traffic or wondering if you might be mirroring too much data and could be getting more for less. The harsh truth is that in order to be able to detect everything, you have to capture everything and in order to be able to investigate security issues thoroughly, you need to capture every network packet.
Beats are lightweight, purpose-built agents that acquire data and then feed it to Elasticsearch. Beats use the libbeat framework that makes it easy to create customized beats for any type of data you’d like to send to Elasticsearch. Auditbeat is a lightweight shipper from the Beats family that you can install on your servers to audit the activities of users and processes on your systems.
AWS S3 Server Access logs provide detailed records for requests made to S3 buckets. They’re useful for many applications. For example, access log information can be useful in security and access audits. It can also help generate customer insights and better understand your Amazon S3 bill. Coralogix makes it easy to integrate with your S3 server access logs via a Lambda function.
AWS VPC Flow Logs record details about the traffic passing through your application, including requests that were allowed or denied according to your ACL (access control list) rules. It also has information about the IP addresses, and ports for each request, the number of packets, bytes sent, and timestamps for each request. This information brings deep visibility and the ability to improve your security posture over time.
Regular expressions can be crucial for wrangling log data efficiently. You may want to extract specific data from your logs to make it easier to analyze and visualize. Sometimes you might want to capture an email when a particular message is logged. Other times, you may find yourself needing to hide sensitive data in logs before they are saved. And more often than not, you need to match using a RegEx pattern rather than an exact text search. This guide is split into three parts.
