Malware Detection Using Machine Learning

Decision making using Machine Learning can be efficiently applied to security. Malware has become a big risk in today’s times. In order to provide protection for the same, we present a machine-learning based technique for predicting Windows PE files as benign or malignant based on fifty-seven of their attributes. We have used the Brazilian Malware dataset, which had around 1,00,000 samples and 57 labels. We have made seven models, and have achieved 99.7% accuracy for the Random Forest model, which is very high when compared to other existing systems. Thus using the Random Forest model one can make a decision on whether a particular file is malware or benign.