MCoMPytorch

This repository contains the code for (Springer Link)

@inproceedings{li2022mcom,
  title={MCoM: A Semi-Supervised Method for Imbalanced Tabular Security Data},
  author={Li, Xiaodi and Khan, Latifur and Zamani, Mahmoud and Wickramasuriya, Shamila and Hamlen, Kevin W and Thuraisingham, Bhavani},
  booktitle={IFIP Annual Conference on Data and Applications Security and Privacy},
  pages={48--67},
  year={2022},
  organization={Springer}
}

Dependencies

The project was run on a conda virtual environment on Ubuntu 18.04.5 LTS.

If you have conda pre-installed cd into the directory where you have downloaded the source code and run the following

conda create -n mcom python==3.7
conda activate mcom

Running Experiments

To run experiments they are launched from the train.py file. For example, to run MCoM on MNIST use the following command

python train.py -c ./configs/security/mcom_security.json --pretrain

The trainer, data loader, model, optimizer, settings are all specified in the ./configs/security/mcom_security.json file. The --pretrain options specifies whether to run the pretraining phase (i.e. training the encoder).

TODO

• Add instructions to run code
• File structure
• Config file instructions
• Comment/clean code
• Jupyter lab demo