Last month, members of the Elastic Security team hosted a threat hunting capture the flag (CTF) event at BSides SATX. We provided the community with an environment to learn and practice threat hunting with our team, and cultivated new relationships with attendees. By sharing information with security practitioners, we can help prepare them to defend their organization’s data from attack through knowledge transfer.

In this blog, I will walk you through the process of configuring both Filebeat and Zeek (formerly known as Bro), which will enable you to perform analytics on Zeek data using Elastic Security. The default configuration for Filebeat and its modules work for many environments; however, you may find a need to customize settings specific to your environment.

The issue of unsecured databases is growing. In 2019, 17 percent of all data breaches were caused by human error — twice as many as just a year before. And the IBM/Ponemon 2019 report found that the estimated probability of a company having repeated data breaches within two years grew by 31 percent between 2014 and 2019. Why is this happening?

On July 14, 2020, Microsoft released a security update related to a remote code execution (RCE) and denial of service (DoS) vulnerability (CVE-2020-1350) in Windows DNS Server (2003 - 2019).

Detection engineering at Elastic is both a set of reliable principles — or methodologies — and a collection of effective tools. In this series, we’ll share some of the foundational concepts that we’ve discovered over time to deliver resilient detection logic. In this blog post, we will share a concept we call stateful detection and explain why it's important for detection.

Today we are pleased to announce new traffic management features for Elastic Cloud. Now you can configure IP filtering within your Elastic Cloud deployment on Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. We are also announcing integration with AWS PrivateLink. These features help give you greater control over the network security layer of your Elastic workloads.

This post continues this two-part blog series on further understanding the differences between macOS and Windows on the system level for effective endpoint security analysis. In Part 1, we covered process events. Here in Part 2, we’ll discuss file and network events. As with Part 1, my hope is to help cybersecurity professionals expand and enrich their experiences on a less familiar platform, ultimately helping them to be better prepared to face differences from past experiences.

In Part 1 of this blog series, we took a look at how we could use Elastic Stack machine learning to train a supervised classification model to detect malicious domains. In this second part, we will see how we can use the model we trained to enrich network data with classifications at ingest time. This will be useful for anyone who wants to detect potential DGA activity in their packetbeat data.