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arXiv · 1808.05760

Data Poisoning Attacks in Contextual Bandits

Yuzhe Ma·Kwang-Sung Jun·Lihong Li·Xiaojin Zhu
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Abstract

We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards in the data, an attacker can force the bandit algorithm to pull a target arm for a target contextual vector. The target arm and target contextual vector are both chosen by the attacker. That is, the attacker can hijack the behavior of a contextual bandit. We also investigate the feasibility and the side effects of such attacks, and identify future directions for defense. Experiments on both synthetic and real-world data demonstrate the efficiency of the attack algorithm.

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Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu. 2018-08-17. Data Poisoning Attacks in Contextual Bandits. https://arxiv.org/abs/1808.05760

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