Book Image

Building a Next-Gen SOC with IBM QRadar

By : Ashish M Kothekar
Book Image

Building a Next-Gen SOC with IBM QRadar

By: Ashish M Kothekar

Overview of this book

This comprehensive guide to QRadar will help you build an efficient security operations center (SOC) for threat hunting and need-to-know software updates, as well as understand compliance and reporting and how IBM QRadar stores network data in real time. The book begins with a quick introduction to QRadar components and architecture, teaching you the different ways of deploying QRadar. You’ll grasp the importance of being aware of the major and minor upgrades in software and learn how to scale, upgrade, and maintain QRadar. Once you gain a detailed understanding of QRadar and how its environment is built, the chapters will take you through the features and how they can be tailored to meet specifi c business requirements. You’ll also explore events, flows, and searches with the help of examples. As you advance, you’ll familiarize yourself with predefined QRadar applications and extensions that successfully mine data and find out how to integrate AI in threat management with confidence. Toward the end of this book, you’ll create different types of apps in QRadar, troubleshoot and maintain them, and recognize the current security challenges and address them through QRadar XDR. By the end of this book, you’ll be able to apply IBM QRadar SOC’s prescriptive practices and leverage its capabilities to build a very efficient SOC in your enterprise.
Table of Contents (18 chapters)
Part 1: Understanding Different QRadar Components and Architecture
Part 2: QRadar Features and Deployment
Part 3: Understanding QRadar Apps, Extensions, and Their Deployment

Integration with the ML app

The ML app brings with it capabilities of predictive modeling. This application requires intensive computation and works best when you use a separate App Host to host the applications. The ML app is installed after the UBA app is installed.

Important note

It is recommended that after you install UBA and configure it to import users, you install the ML app after at least 24 hours. This gives UBA enough time to create user profiles and assign risk scores.

The ML app has different models that it uses:

  • Individual (Numeric) user model: This model calculates a value for a user.
  • Individual (Observable) user model: This model calculates a set of attributes and their event counts.
  • Peer Group model: This model is used to build a set of attributes and event counts and alert if the deviation of the user is more for the defined peer group.What we mean by deviation is the deviation in the risk score of the user. This peer group could be all the...