At this point, it is clear: cyber attacks from nation-state adversaries persistently threaten local, state, and federal governments, as well as educational institutions. It is not a matter of if bad actors can penetrate existing security controls, as they are already doing so and will continue to do so. Whether it is due to one unpatched machine or one user clicking on a link in an email, we believe cyberattacks are inevitable.

Malware often hides communications with its command and control (C2) server over HTTPS. The encryption in HTTPS usually conceals the compromise long enough for the malware to accomplish its goal. This makes detecting malware that uses HTTPS challenging, but once in a while, you will catch a break, as in the case here with AsyncRAT, a Windows remote access tool that has been deployed over the past year to target organizations that manage critical infrastructure in the United States.

In this installment of Hunting with Splunk we’re showing you how to detect suspicious and potentially malicious network traffic to “new” domains. First, let’s delve into what we mean by “new” domains and why you should make a habit of detecting this activity in the first place. (Part of our Threat Hunting with Splunk series, this article was originally written by Andrew Dauria. We've updated it recently to maximize your value.)

Welcome to the final installment in our “Add to Chrome?” research! In this post, we'll experiment with a method to find masquerading, or suspicious clusters of Chrome extensions using Model-Assisted Threat Hunting (M-ATH) with Splunk and the Data Science & Deep Learning (DSDL) App. M-ATH is a SURGe-developed method from the PEAK framework, which uses models or algorithms to help find threat-hunting leads, or to help make complex problems more approachable.

Hypothesis-driven hunting is probably the most well-known type of threat hunting, and it’s one of the three types defined in the PEAK threat hunting framework. In this article, we’ll walk through a sample hypothesis-driven hunt, step-by-step. For our data, we’ll be using the Boss of the SOC Version 3 (BOTSv3) dataset, which you can use to recreate the hunt and work through it on your own. Below is a diagram of the Hypothesis-Driven hunting process.

It’s 10:30 p.m. and you’re heading to bed. Unfortunately, a threat actor has your organization in their crosshairs. While you’re brushing your teeth, they’re crafting a social engineering email to pilfer your employees’ credentials. While you’re putting on your pajamas, they’re finding a path to log in. While you’re asleep, is your organization protected?

The role of artificial intelligence (AI) has become increasingly prominent, particularly in the realm of cyber threat hunting. Cyber threats continue to evolve in complexity and sophistication, posing significant challenges to traditional cybersecurity measures. As a result, organizations are turning to AI-driven solutions to enhance their threat detection and response capabilities.

When hunting, advanced security Splunkers use apps. Specifically, three related apps from an incredibly generous man named Cedric Le Roux! (You can guess from the name that yes, he's French.) And frankly, you probably only know one: URL Toolbox. One of the most popular Splunk security apps of all time, URL Toolbox’s URL parsing capabilities have been leveraged by thousands who want to separate subdomain, domain, and top level domain (TLD) from a URL.

I hope you're all enjoying this series on Hunting with Splunk as much as we enjoy bringing it to you. This article discusses a foundational capability within Splunk — the eval command. If I had to pick a couple of Splunk commands that I would want to be stuck on a desert island with, the eval command is up there right next to stats and sort. (Part of our Threat Hunting with Splunk series, this article was originally written by John Stoner. We’ve updated it recently to maximize your value.)

At Corelight, we’re always striving to make the life of threat hunters and security analysts a little easier. It’s the reason we developed our Open NDR Platform that provides comprehensive, correlated network data and forensic evidence about everything happening on the network. If you’re familiar with Corelight, you probably already know that.