A Method for Windows Malware Detection Based on Deep Learning

As the Internet rapidly develops, the types and quantity of malware continue to diversify and increase, and the technology of evading security software is becoming more and more advanced. This paper proposes a malware detection method based on deep learning, which combines malware visualization technology with convolutional neural network. The structure of neural network is based on VGG16 network. This paper proposes the hybrid visualization of malware, combining static and dynamic analysis. In hybrid visualization, we use the Cuckoo Sandbox to carry out dynamic analysis on the samples, convert the dynamic analysis results into a visualization image according to a designed algorithm, and train the neural network on static and hybrid visualization images. Finally, we test the performance of the malware detection method we propose, evaluating its effectiveness on detecting unknown malware.

Automated summary

This paper proposes a malware detection method based on deep learning, which combines malware visualization technology with convolutional neural network, and integrates static and dynamic analysis, enhancing the effectiveness of malware detection by visualizing the dynamic analysis results.