Deep Convolutional Generative Adversarial Networks in Image-Based Android Malware Detection

Abstract

The recent advancements in generative adversarial networks have showcased their remarkable ability to create images that are indistinguishable from real ones. This has prompted both the academic and industrial communities to tackle the challenge of distinguishing fake images from genuine ones. We introduce a method to assess whether images generated by generative adversarial networks, using a dataset of real-world Android malware applications, can be distinguished from actual images. Our experiments involved two types of deep convolutional generative adversarial networks, and utilize images derived from both static analysis (which does not require running the application) and dynamic analysis (which does require running the application). After generating the images, we trained several supervised machine learning models to determine if these classifiers can differentiate between real and generated malicious applications. Our results indicate that, despite being visually indistinguishable to the human eye, the generated images were correctly identified by a classifier with an F-measure of approximately 0.8. While most generated images were accurately recognized as fake, some were not, leading them to be considered as images produced by real applications.