An inception V3 approach for malware classification using machine learning and transfer learning

Abstract

Malware instances have been extremely used for illegitimate purposes, and new variants of malware are observed every day. Machine learning in network security is one of the prime areas of research today because of its performance and has shown tremendous growth in the last decade. In this paper, we formulate the malware signature as a 2D image representation and leverage deep learning approaches to characterize the signature of malware contained in BIG15 dataset across nine classes. The current research compares the performance of various machine learning and deep learning technologies towards malware classification such as Logistic Regression (LR), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), transfer learning on CNN and Long Short Term Memory (LSTM). The transfer learning approach using InceptionV3 resulted in a good performance with respect to the compared models like LSTM with a classification accuracy of 98.76% on the test dataset and 99.6% on the train dataset.

Keywords

• Microsoft BIG15
• Logistic regression
• Artificial neural network
• Long short term memory
• Convolutional neural network
• InceptionV3