In an earlier blog post, we spoke about building your own ProblemChild framework from scratch in the Elastic Stack to detect living off the land (LOtL) activity. As promised, we have now also released a fully trained detection model, anomaly detection configurations, and detection rules that you can use to get ProblemChild up and running in your environment in a matter of minutes.
This quick blog is the first in a two-part series discussing a userland Windows exploit initially disclosed by James Forshaw and Alex Ionescu. The exploit enables attackers to perform highly privileged actions that typically require a kernel driver.
King & Wood Mallesons (KWM) is among the world’s most innovative law firms and is represented by 2,400 lawyers in 28 locations across the globe. The international law firm, based in Australia, helps clients flourish in Asian markets by helping them understand and navigate local challenges and by delivering solutions that provide clients with a competitive advantage.
When it comes to malware attacks, one of the more common techniques is “living off the land” (LOtL). Utilizing standard tools or features that already exist in the target environment allows these attacks to blend into the environment and avoid detection. While these techniques can appear normal in isolation, they start looking suspicious when observed in the parent-child context. This is where the ProblemChild framework can help.
Today I’m happy to share more about our partnership with Swimlane, which further reinforces our commitment to empowering security teams everywhere. Today’s security teams rely on the power of Elastic’s high-speed, cloud-scale analytics to solve their most complex and pressing security issues. Swimlane’s security automation platform provides a way for these same teams to accelerate and optimize their workflows for max efficiency and to solve SOAR use cases.
Whether this is the third time you are looking at the MITRE Engenuity ATT&CK® evaluation results or your first, you may be asking yourself: what was unique about this year’s evaluation? Well, let’s first start with: who is MITRE Engenuity? They are a tech foundation that collaborates with the private sector on many initiatives — most notably cybersecurity — and in recent years have become synonymous with cyber threat evaluations.
In our previous blog post on Windows access tokens for security practitioners, we covered: Having covered some of the key concepts in Windows security, we will now build on this knowledge and start to look at how attackers can abuse legitimate Windows functionality to move laterally and compromise Active Directory domains. This blog has deliberately attempted to abstract away the workings of specific Windows network authentication protocols (e.g., NTLM and Kerberos) where possible.
TierPoint is a leading provider of secure, connected data center and cloud solutions at the edge of the Internet with thousands of customers. At TierPoint, I’m responsible for maintenance and development of the information security program, which includes threat analytics, incident response, and digital forensics. We’re constantly looking for new and even more effective ways to aggregate, process, and make decisions from massive amounts of data streaming in from diverse sources.
At Elastic Security, we approach the challenge of threat detection with various methods. Traditionally, we have focused on machine learning models and behaviors. These two methods are powerful because they can detect never-before-seen malware. Historically, we’ve felt that signatures are too easily evaded, but we also recognize that ease of evasion is only one of many factors to consider.
The Elastic Common Schema (ECS) provides an open, consistent model for structuring your data in the Elastic Stack. By normalizing data to a single common model, you can uniformly examine your data using interactive search, visualizations, and automated analysis. Elastic provides hundreds of integrations that are ECS-compliant out of the box, but ECS also allows you to normalize custom data sources. Normalizing a custom source can be an iterative and sometimes time-intensive process.
