Deception for Cyber Defence: Challenges and Opportunities

TL;DR

This paper discusses how machine learning can enable scalable, automated deception techniques in cyber defense, addressing current challenges and exploring future opportunities for enhancing security measures.

Contribution

It highlights the potential of machine learning to automate the creation of realistic deception artifacts, overcoming cost barriers in cyber defense strategies.

Findings

Machine learning enables scalable deception generation.

Deception can complement existing security measures.

Challenges include model accuracy and realism.

Abstract

Deception is rapidly growing as an important tool for cyber defence, complementing existing perimeter security measures to rapidly detect breaches and data theft. One of the factors limiting the use of deception has been the cost of generating realistic artefacts by hand. Recent advances in Machine Learning have, however, created opportunities for scalable, automated generation of realistic deceptions. This vision paper describes the opportunities and challenges involved in developing models to mimic many common elements of the IT stack for deception effects.