In this blog, I’ll share a few NetOps observations of the Black Hat network that I made during my time serving in the Black Hat Network Operations Center (NOC). My hope in doing so is to spark some ideas on how you can use an existing tool like Zeek for a new purpose. These insights were particularly revealing, despite not being linked to any security incidents.

It’s natural to want to believe that every new account creation or online purchase signals the legitimate growth of your business. But the alarming rise in financial and data losses attributed to fraud, suggests a different reality. There are thousands of bad actors actively looking to nickel and dime businesses and consumers. In 2022, a staggering 2.4 million fraud reports flooded the Consumer Sentinel Record.

Back in 2013, Gartner’s Anton Chuvakin set out to name a new set of security solutions to detect suspicious activity on endpoints. After what he called “a long agonizing process that involved plenty of conversations with vendors, enterprises, and other analysts” Chuvakin came up with this phrase: endpoint threat detection and response. Since then, this moniker has been shortened to endpoint detection and response or EDR. But as the name got smaller, the market got bigger.

Effective threat detection is critical to achieving a mature cybersecurity posture. Yet with so many threat detection options on the market, from managed detection and response (MDR) to managed security service providers (MSSPs) to security information and event management (SIEM), choosing the most effective one for your organization can be challenging.

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.

The explosion of interest in artificial intelligence (AI) and specifically large language models (LLMs) has recently taken the world by storm. The duality of the power and risks that this technology holds is especially pertinent to cybersecurity. On one hand the capabilities of LLMs for summarization, synthesis, and creation (or co-creation) of language and content is mind-blowing.

On July 14, CISA published an industrial control system (ICS) advisory about two new critical vulnerabilities affecting Rockwell Automation ControlLogix communication modules: CVE-2023-3595 and CVE-2023-3596. CISA and Rockwell Automation recommended that asset owners patch vulnerable devices and add controls such as segmenting networks and using network intrusion detection.

The Elastic InfoSec Threat Detection team is responsible for building, tuning, and maintaining the security detections used to protect all Elastic® systems. Internally, we call ourselves Customer Zero and we strive to always use the newest versions of our products. This blog details how we are building packages of detection rules that work together to create a high fidelity alert for strange user behavior.

It doesn't matter if you have a smartphone, a tablet, a laptop, or a desktop computer. Whatever your computing device of choice, you don't want it impacted by malware. And although many of us are familiar with the concept of protecting our PCs and laptops with security software that aims to identify attacks in real-time, it's not a defence that is as widely adopted on mobile devices.

It has been a distinct honor to be a part of the Corelight team that helped defend this year’s Black Hat events. I started the event season in the Network Operations Center (NOC) at Black Hat Asia, and then capped it off at Black Hat in Las Vegas. In this blog I’ll share my experience and learnings from participating in both NOCs.