Malware-Classification

This one is just for fun. Simple conclusion can be made is many false positive because of similar behavior for Zeus and Crypto.

Best parameter to classifiy this data using Random Forest:

Grid score: 0.5354242509101093

Grid parameter:

'clf-rf__random_state': 42,
'tfidf__use_idf': False,
'vect__ngram_range': (1, 1)

Accuracy of RF classifier on training set: 0.68

Accuracy of RF classifier on test set: 0.62

Classification report :

          precision    recall  f1-score   support

   APT1       0.96      0.89      0.92        75
 Crypto       0.57      0.75      0.65       513
 Locker       0.97      0.54      0.69       114
   Zeus       0.59      0.46      0.52       489

avg / total   0.64      0.62      0.62      1191

Confusion Matrix for training using Random Forest:

Confusion Matrix for testing using Random Forest:

Using dataset from https://github.com/marcoramilli/MalwareTrainingSets