一种用于实现并防御电网中生成式AI攻击的零信任框架
A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid
AI总结：
针对电网供应链中的生成式AI攻击，本文提出零信任框架，结合GAN攻击生成、尾部风险度量、持续信任验证和集成学习检测，实现攻击识别与防御。
摘要：
理解基于生成式AI（GenAI）的攻击对电网的潜在影响，是一项必须应对的根本性挑战；为了保护电网，需要在新型攻击向量中识别并验证风险。本文提出了一种面向电网供应链（PGSC）的新型零信任框架。该框架有助于早期检测潜在的GenAI驱动攻击向量（例如重放攻击和协议类型攻击）、评估基于尾部风险的稳定性度量，并缓解此类威胁。首先，设计了一种新的PGSC零信任系统模型，并将其表述为一个零信任问题，旨在通过识别并防御GenAI驱动的网络攻击来保证稳定的PGSC。其次，在该模型中开发了一种面向特定领域、基于生成对抗网络（GAN）的攻击生成机制，以创建新的漏洞网络空间，从而进一步理解该威胁。第三，提出并实现了基于尾部的风险识别指标，用于量化潜在攻击的极端风险，同时利用信任度量方法进行持续验证。第四，设计了一种基于集成学习的自助聚合方案，用于检测那些生成合成身份的攻击，这些身份具有可信的用户和分布式能源设备画像。实验结果表明了所提零信任框架的有效性：其在攻击向量生成上达到95.7%的准确率，对于95%稳定的PGSC实现9.61%的风险度量，并在防御GenAI驱动攻击方面达到99%的置信度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
