Deception for Cyber Defence: Challenges and Opportunities
David Liebowitz
Surya Nepal
Kristen Moore
Cody James Christopher
S. Kanhere
David D. Nguyen
Roelien C. Timmer
Michael Longland
Keerth Rathakumar

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.
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