ml-malware-classifier

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ml-malware-classifier

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Machine Learning Android Malware Classifier

// repository documentation

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# ml-malware-classifier

### Reference

```
Daniel Arp, Michael Spreitzenbarth, Malte Huebner, Hugo Gascon, and Konrad Rieck 
"Drebin: Efficient and Explainable Detection of Android Malware in Your Pocket", 
21th Annual Network and Distributed System Security Symposium (NDSS), February 2014
```

* The original paper can be found [here](https://www.tu-braunschweig.de/Medien-DB/sec/pubs/2014-ndss.pdf). 
* The original dataset can be found [here](https://www.sec.cs.tu-bs.de/~danarp/drebin/).

### Usage
The code is inside `code` folder. Use `main.py` to run the script.
```
usage: main.py [-h] [-d DATA] [--type TYPE] [-s S] method
Android Malware Classificator

positional arguments:  method       mnb=MultinomialNB, bnb=BernoulliNB, sgdc=SGDClassifier, 
                                    lsvc=LinearSVC, svm=SVM, rf=RandomForest

optional arguments:
  -h, --help   show this help message and exit
  -d DATA      path to the dataset folder
  --type TYPE
  -s S         Feature subset
  ```
  
The dataset is inside `data` folder. There are two subfolders, `small_drebin`, which contains a very little portion of the original dataset, and `medium_drebin`, which contains roughly 5500 files. By default the script uses the medium folder. To use custom data, put it inside `data` folder. It should have this format:

ml-malware-classifier

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