# Online Data Poisoning Attack

arXiv: https://arxiv.org/abs/1903.01666 · HTML: https://mlsanity.com/paper/1903.01666/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detector](https://mlsanity.com/paper/2301.01044/) (2023)
- [Influence Based Defense Against Data Poisoning Attacks in Online Learning](https://mlsanity.com/paper/2104.13230/) (2021)
- [Gradient-based Data Subversion Attack Against Binary Classifiers](https://mlsanity.com/paper/2105.14803/) (2021)
- [The Art of Manipulation: Threat of Multi-Step Manipulative Attacks in Security Games](https://mlsanity.com/paper/2202.13424/) (2022)

## Abstract (arXiv)

We study data poisoning attacks in the online setting where training items arrive sequentially, and the attacker may perturb the current item to manipulate online learning. Importantly, the attacker has no knowledge of future training items nor the data generating distribution. We formulate online data poisoning attack as a stochastic optimal control problem, and solve it with model predictive control and deep reinforcement learning. We also upper bound the suboptimality suffered by the attacker for not knowing the data generating distribution. Experiments validate our control approach in generating near-optimal attacks on both supervised and unsupervised learning tasks.