Classifying Malware Images with Convolutional Neural Network Models

Ahmed Bensaoud ； Nawaf Abudawaood ； Jugal Kalita

《International Journal of Network Security》 22卷6期 (2020/11) Pp. 1022-1031

https://doi.org/10.6633/IJNS.202011_22(6).17

摘要

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 flies 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.

關鍵字

Convolutional Neural Network ； Malware Classification ； Malware Detection ； ImageNet