Data Poisoning Attacks to Deep Learning Based Recommender Systems

This repository is the major code implementation for the paper:

Hai Huang, Jiaming Mu, Neil Zhenqiang Gong, Qi Li, Bin Liu, Mingwei Xu. Data Poisoning Attacks to Deep Learning Based Recommender Systems. In ISOC Network and Distributed System Security Symposium (NDSS), 2021.

Setup

Environment requirements

Python ≥ 3.6, Pandas ≥ 1.0, NumPy ≥ 1.19, PyTorch ≥ 1.4.

Dataset Preparation

Please follow the instructions in data processing to prepare the datasets for experiments.

Examples

Before conducting attacks, you should firstly copy the processed dataset to the ./attacks/Data folder, and then execute the following commands under the ./attacks folder.

Take the MovieLens-100K dataset for example, you can use the following command to conduct our attack:

python main.py --path Data/ --dataset ml-100k --epochs 20 --batch_size 256 --num_factors 8 --layers "[64,32,16,8]" --l2_reg 0 --num_neg 4 --lr 0.001 --alpha 1E-2 --kappa 1.0 --rounds 5 --m 5 --n 30 --topK 10 --targetItem 100 --reg_u 1E2 --prob 0.9 --s 1

You can use the following command to conduct the random attack:

python random_attack.py --path Data/ --dataset ml-100k --epochs 20 --batch_size 256 --num_factors 8 --layers "[64,32,16,8]" --l2_reg 0 --num_neg 4 --lr 0.001 --m 5 --n 30 --topK 10 --targetItem 100

Similarly, you can use the following command to conduct the bandwagon attack:

python bandwagon_attack.py --path Data/ --dataset ml-100k --epochs 20 --batch_size 256 --num_factors 8 --layers "[64,32,16,8]" --l2_reg 0 --num_neg 4 --lr 0.001 --m 5 --n 30 --topK 10 --targetItem 100

You can refer to the instructions in attacks for more details.

Citation

Please cite our paper if you use this code in your own research work:

@inproceedings{HMGLLX21,
author = {Hai Huang and Jiaming Mu and Neil Zhenqiang Gong and Qi Li and Bin Liu and Mingwei Xu},
title = {{Data Poisoning Attacks to Deep Learning Based Recommender Systems}},
booktitle = {{Network and Distributed System Security Symposium (NDSS)}},
publisher = {Internet Society},
year = {2021}
}

Acknowledgements

We thank the maintainers of the following open-sourced repositories:

1. https://github.com/hexiangnan/neural_collaborative_filtering
2. https://github.com/yihong-chen/neural-collaborative-filtering
3. https://github.com/fuying-wang/Data-poisoning-attacks-on-factorization-based-collaborative-filtering