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Explanation-Guided_Backdoor_Poisoning

Code for the paper Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers, appearing at USENIX Security 2021.

ArXiv version at: https://arxiv.org/abs/2003.01031

Dependencies

This codebase has been developed and tested only with python 3.8.

This code depends on the EMBER package. Please install the requirements of this repository with pip install -r requirements.txt and then install the EMBER package by cloning the repository and running python setup.py install.

Data

After obtaining and extracting the datasets, specify the relevant paths in mw_backdoor/constants.py.

EMBER dataset

In this work we used version 1.0 of the EMBER dataset. Please download the compressed file, links here, and unpack it.

Drebin dataset

Please find the Drebin dataset at https://www.sec.cs.tu-bs.de/~danarp/drebin/

Contagio dataset

For PDF data we used the dataset released by Contagio. The sha256 of the pdf files we used can be found in the data folder. For ease of access, to avoid the lengthy operation of extracting the feature vectors from PDF files, the dataset feature numpy files are provided in data/.

To re-run the feature extraction use the script:

python extract_pdf_features.py -p [NUM_WORKERS] [--force]

The script expects the bening files to be in a sub-directory called contagio_goodware and the malicious ones in contagio_malware.

Model training

To train the models used in the paper, please run the script train_model.py.

To train the LightGBM and EmberNN models on EMBER:

python train_model.py -m lightgbm -d ember
python train_model.py -m embernn -d ember

To train the Random Forest model on Contagio PDFs:

python train_model.py -m pdfrf -d ogcontagio

To train the Linear SVM classifier on Drebin:

python train_model.py -m linearsvm -d drebin

Backdoor attacks

To run a backdoor attack, use the script backdoor_attack.py passing as argument the path to a configuration file.

To simply create a backdoor pattern, without running the full attack use generate_watermarks.py. fixed_wm_attack.py can be used instead to run the attack given a pre-computed backdoor.

Attack scripts generally require a configuration file with the following fields:

{
  "model": "string -- name of the model to target",
  "poison_size": "list of floats -- poison sizes w.r.t. the training set",
  "watermark_size": "list of integers -- number of features to use",
  "target_features": "string -- subset of features to target [all, feasible]",
  "feature_selection": "list of strings -- name of feature selectors",
  "value_selection": "list of strings -- name of value selectors",
  "iterations": "int -- number of times each attack is run",
  "dataset": "string -- name of the target dataset",
  "k_perc": "float -- fraction of data known to the adversary",
  "k_data": "string -- type of data known to the adversary [train]",
  "save": "string -- optional, path where to save the attack artifacts for defensive evaluations",
  "defense": "bool -- optional, set True when running the defensive code"
}

EMBER

To reproduce the attacks with unrestricted threat model, shown in Figure 2, please run:

python backdoor_attack.py -c configs/embernn_fig2.json

python backdoor_attack.py -c configs/lightgbm_fig2.json

To reproduce the constrained attacks, run:

python backdoor_attack.py -c configs/embernn_fig4.json

python backdoor_attack.py -c configs/lightgbm_fig4.json

Note: the transfer attacks can be carried out by first generating the backdoor pattern with generate_watermarks.py, using the configuration file for the proxy model. Successively the actual attack can be started using fixed_wm_attack.py and the configuration file for the victim model.

Drebin

The constrained attack with combined strategy on Drebin data, shown in Figure 5, can be run with:

python backdoor_attack.py -c configs/drebin_fig5.json

Contagio

To run the constrained attack with combined strategy on Contagio PDFs the watermark must be generated first. First run:

python generate_watermarks.py -c configs/ogcontagio_fig5.json

to create the watermark file.

Then run the backdoor_pdf_files.py script, which uses the generated backdoor trigger. This will attempt to backdoor all the files in the training set, operating directly on the pdf files using the Mimicus utility, then it will create two csv files with the successfully backdoored vectors.

Finally, run the attack using the newly generated data, use the backdoor_pdf_evaluation.py script.

Note: to reduce the computation time, these scripts use multiprocessing. The number of spawned processes can be set inside the script.

Mitigations

In order to run any mitigation experiment, first run the desired attack for 1 iteration setting the save parameter of the configuration file to a valid path in the system, and "defense": true. The attack script will save there a set of artifacts such as the watermarked training and test sets, and the backdoor trigger details.

Isolation Forest can be run on the backdoored data to perform anomaly detection with defense_isoforest.py, and defense_isoforest_ember.py. Make sure to have set the appropriate variables for the specific attack scenario before running the script.

Code for applying he adapted Spectral Signatures, and Activation Clustering, defenses on the EMBER based models can be found din defense_filtering.py.