# Adversarial attacks against Bayesian forecasting dynamic models

arXiv: https://arxiv.org/abs/2110.10783 · HTML: https://mlsanity.com/paper/2110.10783/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Fake News Detection via NLP is Vulnerable to Adversarial Attacks](https://mlsanity.com/paper/1901.09657/) (2019)
- [Adversarial classification: An adversarial risk analysis approach](https://mlsanity.com/paper/1802.07513/) (2018)
- [Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data](https://mlsanity.com/paper/1607.02655/) (2016)

## Built on by (most cited first, 2 of 2)

- [Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting](https://mlsanity.com/paper/2311.09790/) (2023)
- [Manipulating hidden-Markov-model inferences by corrupting batch data](https://mlsanity.com/paper/2402.13287/) (2024)

## Abstract (arXiv)

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.