MALTUTOR

This code repository our paper titled Empowering DNN-Based Android Malware Detection via Uncertainty-Guided Curriculum Learning.

Overview

In this paper, we take the first step to train the uncertainty estimatin model. Subsequently, we clustered malware samples based on the output of the uncertainty model. Finally, we train a robust model.

Dependencies:

We develop the codes on Windows operation system, and run the codes on Ubuntu 20.04. The codes depend on Python 3.8.10. Other packages (e.g., TensorFlow) can be found in the ./requirements.txt.

Datasets:

You can find the hashes of the samples used in our experiments in the Training/config folder.

Usage

1. Estimate uncertainty

 cd Training 

 python train_base_model.py 

2. malware samples clustering

 cd dataset_reconstruction

 python uc_feature_extrctor.py 

uncertainty metrics save to: Traing_robust_Malware_Detector_via_Label_uncertainty/dataset_reconstruction/uc_metrics_csv

 python sample_classifier.py  ## cluster samples 

inter file save to: Traing_robust_Malware_Detector_via_Label_uncertainty/dataset_reconstruction/inter_file

3. robust training

  cd Training

  python CL_robust_model.py 

Hyperparameters:

  ###  train_data_type: training set type.
  ###  val_type: training strategy,(self validation,cross validation)
  ###  n_clusters: the number of clusters for the samples，(3,5,7,9,11)
  ###  feature_type: target model type, (drebin:deepdrebin,apiseq:droidectc)