Generative AI for Threat Hunting and Behaviour Analysis

Abstract

Cyber threats are becoming more advanced, and so is cybersecurity, which is getting more intellectual and better at hiding its presence. The requirement to achieve the balance between proactive resistive and threat-hunting measures in this dynamic environment is very high. Part four outlines how new AI techniques enable the design of the existing processes for hunting potential threats. The main objective is to digress into the core principles of threat hunting, starting from being proactive and including scenarios of deducing the clues based on the hypothesis. Then, the authors will highlight the limitations of conventional methods in detecting the gimmicks that fool even skilled hunters with an unseen threat that is smoking hiddenly in a never-ending evolutionary process. Two well-studied approaches for tackling these challenges are generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs).