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.

翻译：在过去的十年中,反向机器学习(AML)的兴起出现了。这一学科研究如何利用数据来欺骗推论引擎,以及如何保护这些系统免受这种操纵攻击。关于攻击回归和分类系统的广泛工作已经存在,而攻击时间序列预测系统却很少受到重视。我们在本文件中提出了基于决定的进攻战略分析,可以用来对付贝叶斯预测动态模型。