Adversarial Forecasting Through Adversarial Risk Analysis Within a DDDAS Framework

Forecasting methods typically assume clean and legitimate data streams. However, adversaries’ manipulation of digital data streams could alter the performance of forecasting algorithms and impact decision quality. In order to address such challenges, we propose a dynamic data driven application systems (DDDAS) based decision making framework that includes an adversarial forecasting component. Our framework utilizes the adversarial risk analysis principles that allow considering incomplete information and uncertainty. It is demonstrated using a load forecasting example. We solve the adversary’s decision problem in which he poisons data to alter an auto regressive forecasting algorithm output, and discuss defender strategies addressing the attack impact.