INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
It addresses the vulnerability of deep learning-based time-series forecasting models to adversarial attacks in practical online settings with limited memory.
The paper proposes an adversarial attack framework for time-series forecasting in an online bounded-buffer setting, using an informed and selective strategy that targets high-confidence time steps with maximal expected error. The method increases prediction error up to 2.42x while attacking fewer than 10% of time steps.
Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of many real-world systems, where accurate forecasts improve operational efficiency and help mitigate uncertainty and risk. More recently, machine learning (ML), and especially deep learning (DL)-based models, have gained widespread adoption for time-series forecasting, but they remain vulnerable to adversarial attacks. However, many state-of-the-art attack methods are not directly applicable in time-series settings, where storing complete historical data or performing attacks at every time step is often impractical. This paper proposes an adversarial attack framework for time-series forecasting under an online bounded-buffer setting, leveraging an informed and selective attack strategy. By selectively targeting time steps where the model exhibits high confidence and the expected prediction error is maximal, our framework produces fewer but substantially more effective attacks. Experiments show that our framework can increase the prediction error up to 2.42x, while performing attacks in fewer than 10% of time steps.