Optimal Attack against Autoregressive Models by Manipulating the Environment

Understand

We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR).

• In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the environment state in order to manipulate future autoregressive forecasts.
• The attacker's goal is to force autoregressive forecasts into tracking a target trajectory while minimizing its attack expenditure.
• In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models.