Malware Detection using API Calls Visualisations and Convolutional Neural Networks

Abstract

This research explores and analyzes different API Calls sequence transformation methods into images to train deep learning models and determine which combination of these methods and models performs better. We generated images from API Calls sequences using Simhash and FreqSeq. The results were compared by training two well-known Convolutional Network architectures (ResNet50v2 and MobileNetv2). This work presents our experience running these experiments highlighting the results obtained and the challenges we faced.