Abstract

As the landscape of cybersecurity continues to evolve, the need for advanced threat detection and response mechanisms becomes paramount. Traditional rule-based Indicator of Compromise (IoC) systems, akin to positioning sensors at known points around a house, are being surpassed by the innovation of Machine Learning-driven Indicator of Behavior (IoB) systems.

This white paper uses a metaphorical approach to illustrate the fundamental differences between these two security paradigms, drawing parallels between protecting a house with fixed sensors and safeguarding it with a dynamic laser field.

‍

Introduction

In the realm of cybersecurity, the methods employed for threat detection and response play a pivotal role in securing digital assets.

Rule-based systems, relying on predefined Indicators of Compromise (loCs), have long been the stalwarts of security. However, with the advent of Machine Learning-driven Indicators of Behavior (loBs), a paradigm shift is underway.

‍