Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks
Systems and Control
2023-11-30 v1 Cryptography and Security
Machine Learning
Systems and Control
Abstract
In this paper, we present a reinforcement learning (RL) method for solving optimal false data injection attack problems in probabilistic Boolean control networks (PBCNs) where the attacker lacks knowledge of the system model. Specifically, we employ a Q-learning (QL) algorithm to address this problem. We then propose an improved QL algorithm that not only enhances learning efficiency but also obtains optimal attack strategies for large-scale PBCNs that the standard QL algorithm cannot handle. Finally, we verify the effectiveness of our proposed approach by considering two attacked PBCNs, including a 10-node network and a 28-node network.
Keywords
Cite
@article{arxiv.2311.17631,
title = {Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks},
author = {Xianlun Peng and Yang Tang and Fangfei Li and Yang Liu},
journal= {arXiv preprint arXiv:2311.17631},
year = {2023}
}