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Towards Causal Models for Adversary Distractions

2021-04-21 15:02:00
Ron Alford (1), Andy Applebaum (1) ((1) The MITRE Corporation)
arXiv_AI
       
Abstract

Automated adversary emulation is becoming an indispensable tool of network security operators in testing and evaluating their cyber defenses. At the same time, it has exposed how quickly adversaries can propagate through the network. While research has greatly progressed on quality decoy generation to fool human adversaries, we may need different strategies to slow computer agents. In this paper, we show that decoy generation can slow an automated agent's decision process, but that the degree to which it is inhibited is greatly dependent on the types of objects used. This points to the need to explicitly evaluate decoy generation and placement strategies against fast moving, automated adversaries.

Abstract (translated)

URL

https://arxiv.org/abs/2104.10575

PDF

https://arxiv.org/pdf/2104.10575.pdf

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