Deep learning fusion for effective malware detection: leveraging visual features

Malware has become a formidable threat as it has grown exponentially in number and sophistication. Thus, it is imperative to have a solution that is easy to implement, reliable, and effective. While recent research has introduced deep learning multi-feature fusion algorithms, they lack a proper explanation. In this work, we investigate the power of fusing Convolutional Neural Network models trained on the different modalities of malware executables. We are proposing a novel multimodal fusion algorithm, leveraging three different visual malware features: Grayscale Image, Entropy Graph, and SimHash Image, with which we conducted exhaustive experiments independently on each feature and combinations of all three of them using fusion operators such as average, maximum, add, and concatenate for effective malware detection and classification. The proposed strategy has a detection rate 1.00 (on a scale of 0–1) in identifying malware in the given dataset. We explained its interpretability with ...