A Zero Trust Framework for Realization and Defense Against Generative AI
  Attacks in Power Grid
Md. Shirajum Munir Sravanthi Proddatoori Manjushree Muralidhara Walid Saad Zhu Han Sachin Shetty
Understanding the potential of generative AI (GenAI)-based attacks on the
power grid is a fundamental challenge that must be addressed in order to
protect the power grid by realizing and validating risk in new attack vectors.
In this paper, a novel zero trust framework for a power grid supply chain
(PGSC) is proposed. This framework facilitates early detection of potential
GenAI-driven attack vectors (e.g., replay and protocol-type attacks),
assessment of tail risk-based stability measures, and mitigation of such
threats. First, a new zero trust system model of PGSC is designed and
formulated as a zero-trust problem that seeks to guarantee for a stable PGSC by
realizing and defending against GenAI-driven cyber attacks. Second, in which a
domain-specific generative adversarial networks (GAN)-based attack generation
mechanism is developed to create a new vulnerability cyberspace for further
understanding that threat. Third, tail-based risk realization metrics are
developed and implemented for quantifying the extreme risk of a potential
attack while leveraging a trust measurement approach for continuous validation.
Fourth, an ensemble learning-based bootstrap aggregation scheme is devised to
detect the attacks that are generating synthetic identities with convincing
user and distributed energy resources device profiles. Experimental results
show the efficacy of the proposed zero trust framework that achieves an
accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a
95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.