Adversarial attacks against Bayesian forecasting dynamic models

• URL: http://arxiv.org/abs/2110.10783v1
• Date: Wed, 20 Oct 2021 21:23:45 GMT
• Title: Adversarial attacks against Bayesian forecasting dynamic models
• Authors: Roi Naveiro
• Abstract summary: AML studies how to manipulate data to fool inference engines. In this paper, we propose a decision analysis based attacking strategy against Bayesian forecasting dynamic models.
• Score: 1.8275108630751844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The last decade has seen the rise of Adversarial Machine Learning (AML). This discipline studies how to manipulate data to fool inference engines, and how to protect those systems against such manipulation attacks. Extensive work on attacks against regression and classification systems is available, while little attention has been paid to attacks against time series forecasting systems. In this paper, we propose a decision analysis based attacking strategy that could be utilized against Bayesian forecasting dynamic models.

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