Malware detection method based on image analysis and generative adversarial networks

Malware detection is indispensable to cybersecurity. However, with the advent of new malware variants and scenarios with few and imbalanced samples, malware detection for various complex scenarios has been a very challenging problem. In this article, we propose a malware detection method based on image analysis and generative adversarial networks, named MadInG, which can improve the accuracy of malware detection for insufficient samples, sample imbalance, and new variants scenarios. Specifically, we first generate fixed-size grayscale images of malware to reduce the workload of feature engineering or the involvement of domain expert knowledge on malware detection. Then we introduce auxiliary classifier generative adversarial networks into malware detection to enhance the generalization ability of the detector. Finally, we construct a variety of malware scenarios and compare our proposed method with existing popular detection methods. Extensive experimental results demonstrate that our method achieves high accuracy and well balance in malware detection for different scenarios, especially, the detection rate of malware variants reaches 99.5%.