Command and control with poisoned temporal batch data

Abstract

Data manipulation could alter the performance of joint all domain command and-control decisions (JADC2). We present a Bayesian decision theoretic approach for adversarial forecasting when the underlying data collected over time is subject to attack from intelligent adversaries. Proposed adversarial risk analysis-based framework allows incomplete information and uncertainty. We solve the adversary's poisoning decision problem where he manipulates batch data being inputted into the forecasting method of statistical autoregressive models. The findings show the vulnerability of forecasting models under adversarial activities. We discuss potential defender strategies.