A Visualized Malware Detection Framework with CNN and Conditional GAN

TL;DR

This paper introduces a visualized malware detection framework combining CNN and conditional GANs to improve detection accuracy and address class imbalance issues.

Contribution

It presents an integrated visualization and synthetic data generation approach using CNN and conditional GANs for malware detection.

Findings

Achieved 98.51% accuracy with real data

Achieved 97.26% accuracy with augmented data

Effective mitigation of class imbalance in malware datasets

Abstract

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.