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.
@article{arxiv.2208.07127,
title = {Deception for Cyber Defence: Challenges and Opportunities},
author = {David Liebowitz and Surya Nepal and Kristen Moore and Cody J. Christopher and Salil S. Kanhere and David Nguyen and Roelien C. Timmer and Michael Longland and Keerth Rathakumar},
journal= {arXiv preprint arXiv:2208.07127},
year = {2022}
}