Image-Based Malware Classification Method with the AlexNet Convolutional Neural Network Model

In recent years, malware has experienced explosive growth and has become one of the most severe security threats. However, feature engineering easily restricts the traditional machine learning methods-based malware classification and is hard to deal with massive malware. At the same time, the dynamic analysis methods have the problems of complex operation and high cost, which are not suitable for efficiently classifying large quantities of malware. Therefore, we propose a novel static malware detection method based on this study’s AlexNet convolutional neural network (CNN). Unlike existing solutions, we convert all malware bytes into color images, propose an improved AlexNet architecture, and solve the unbalanced datasets with the data enhancement method. Extensive experiments are performed using the Microsoft malware dataset and the Google Code Jam (GCJ) dataset. The experimental results show that the accuracy of the Microsoft malware dataset reaches 99.99%, and the GCJ dataset reaches 99.38%. We also verify that our method can better extract the texture features of malware and improve the accuracy and detection efficiency.