Android Malware Detection using Deep Learning
- Classification of android apps done based on pseudo-dynamic analysis of system API Call sequences.
- Developed a Deep Convolutional Neural network (CNN) and a Recurrent Neural Network (LSTM) model.
- Compared performance of these Deep Neural Network models with Naive Bayes Classifier.
| File | Description |
|---|---|
| generate_dict.py | Generates global dictionary for storing mapping all distinct API Calls to numbers in the dataset and pickles the dictionary |
| load_dict.py | Loads the dictionary of API Calls |
| extract_all_features.py | Extracts all feature vectors (of size n X m X h) for 8 testcases in dataset and pickles them |
| extract_all_features_compressed.py | Extracts all feature vectors (of size n X h) for 600 apps in dataset and pickles them |
| data_reader.py | Loads all pickled feature vectors |
| uncompress.py | Uncompresses features to one-hot form |
| cnn2.py and cnn3.py | Different cnn architectures |
| lstm.py | lstm model |
| nb.py | Naive bayes model |