A Visualized Malware Detection Framework with CNN and Conditional GAN
This work addresses malware detection for security systems, but it is incremental as it combines existing methods like CNNs and GANs in a new application.
The authors tackled malware detection by proposing a framework that visualizes malware as images and uses a conditional GAN to address class imbalance, achieving accuracy rates of 98.51% and 97.26% in different training scenarios.
Malware visualization analysis incorporating with Machine Learning (ML) has been proven to be a promising solution for improving security defenses on different platforms. In this work, we propose an integrated framework for addressing common problems experienced by ML utilizers in developing malware detection systems. Namely, a pictorial presentation system with extensions is designed to preserve the identities of benign/malign samples by encoding each variable into binary digits and mapping them into black and white pixels. A conditional Generative Adversarial Network based model is adopted to produce synthetic images and mitigate issues of imbalance classes. Detection models architected by Convolutional Neural Networks are for validating performances while training on datasets with and without artifactual samples. Result demonstrates accuracy rates of 98.51% and 97.26% for these two training scenarios.