AlertPrioritization

Corresponding code to the paper "Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning " at AAAI 2020 (PDF).

Installation

The following libraries are required.

• Python 3.6
• Tensorflow 1.13
• Scikit-learn
• Matplotlib
• Scipy
• Pandas
• imbalanced-learn

Datasets

Credit Card Fraud Detection and CICIDS2017 Dataset.

Trained models

All the trained neural network models are available here. Please download and unfold the models, then put the converge folder under model.

Usage

Reproduce the experiments

It is higly recommended to use a desktop with more than 20 cpu cores for reporoduction. Once the trained models are downloaded, run the following command to reprodue the experiments:

cd src
python evaluate.py [dataset] [defense] [def_budget] [estimate_adv_budget] [attack] [actual_adv_budget] [n_experiment]

Here, dataset can be either ids or fraud, corresponding to the case study on network intrusion detection or fraud detection. For network intursion detection, defense can be one of rl, uniform and suricata; For fraud detection, defense can be one of rl, uniform, gain and rio. attack can be one of the following: rl, uniform and greedy, as described in the paper. n_experiment is the number of random seeds used for reinforcement learning, and it was set to be 20 in our experiments.

Note that the value of the defense and attack budgets should be the values displayed in Figure 2, 3, 4, 5 of the paper. When using fraud as the case study, please revise the following value in project.conf as follows:

h_def = 16
h_adv = 32

For ids, using the following in project.conf:

h_def = 32
h_adv = 64

Train models from scatch

Please use the following commands for training the models of our approach:

python double_oracle.py [dataset] [def_budget] [adv_budget] [n_experiment]

Contributors

• Liang Tong ([email protected])
• Aron Laszka ([email protected])

Citation

@inproceedings{tong2020finding,
  title={Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning},
  author={Tong, Liang and Laszka, Aron and Yan, Chao and Zhang, Ning and Vorobeychik, Yevgeniy},
  booktitle={34th AAAI Conference on Artificial Intelligence},
  year={2020}
}