  Due to increasing threats from malicious software (malware) in both number
and complexity, researchers have developed approaches to automatic detection
and classification of malware, instead of analyzing methods for malware files
manually in a time-consuming effort. At the same time, malware authors have
developed techniques to evade signature-based detection techniques used by
antivirus companies. Most recently, deep learning is being used in malware
classification to solve this issue. In this paper, we use several convolutional
neural network (CNN) models for static malware classification. In particular,
we use six deep learning models, three of which are past winners of the
ImageNet Large-Scale Visual Recognition Challenge. The other three models are
CNN-SVM, GRU-SVM and MLP-SVM, which enhance neural models with support vector
machines (SVM). We perform experiments using the Malimg dataset, which has
malware images that were converted from Portable Executable malware binaries.
The dataset is divided into 25 malware families. Comparisons show that the
Inception V3 model achieves a test accuracy of 99.24%, which is better than the
accuracy of 98.52% achieved by the current state-of-the-art system called the
M-CNN model.
