Adversarial attacks against Bayesian forecasting dynamic models
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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regressionTime SeriesTime Series AnalysisTime Series ForecastingSimilar Papers 제목 키워드 기반
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