论文标题
通过对抗机器学习，启用网络攻击的负载预测
Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning
论文作者
论文摘要
面对越来越宽的网络攻击表面，对电力公司的网络攻击负载预测比以往任何时候都更为必要，更具挑战性。在本文中，我们提出了一种对抗机器学习（AML）方法，该方法可以对广泛的攻击行为做出反应，而无需检测到异常值。它在增强系统对网络攻击的鲁棒性和在没有攻击时保持合理程度的预测准确性之间达到了平衡。选择并评估了对抗性训练的攻击模型和配置，以在模拟研究中达到所需的性能水平。结果证明了所提出的方法的有效性和出色性能。
In the face of an increasingly broad cyberattack surface, cyberattack-resilient load forecasting for electric utilities is both more necessary and more challenging than ever. In this paper, we propose an adversarial machine learning (AML) approach, which can respond to a wide range of attack behaviors without detecting outliers. It strikes a balance between enhancing a system's robustness against cyberattacks and maintaining a reasonable degree of forecasting accuracy when there is no attack. Attack models and configurations for the adversarial training were selected and evaluated to achieve the desired level of performance in a simulation study. The results validate the effectiveness and excellent performance of the proposed method.
