Synthetic Data: AI's New Weapon Against Android Malware

TL;DR

This paper introduces MalSynGen, a cGAN-based method for generating high-quality synthetic Android malware data to enhance classifier performance and address data scarcity issues.

Contribution

It presents a novel synthetic data generation approach using cGANs specifically designed for Android malware detection datasets.

Findings

Synthetic data improves malware classifier accuracy

MalSynGen generalizes across multiple datasets

Generated data maintains statistical fidelity

Abstract

The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the critical importance of effective detection methods. Attackers are now using Artificial Intelligence to create sophisticated malware variations that can easily evade traditional detection techniques. Although machine learning has shown promise in malware classification, its success relies heavily on the availability of up-to-date, high-quality datasets. The scarcity and high cost of obtaining and labeling real malware samples presents significant challenges in developing robust detection models. In this paper, we propose MalSynGen, a Malware Synthetic Data Generation methodology that uses a conditional Generative Adversarial Network (cGAN) to generate synthetic tabular data. This data preserves the statistical properties of real-world data and improves the performance of Android malware classifiers. We evaluated the effectiveness of this approach using various datasets and metrics that assess the fidelity of the generated data, its utility in classification, and the computational efficiency of the process. Our experiments demonstrate that MalSynGen can generalize across different datasets, providing a viable solution to address the issues of obsolescence and low quality data in malware detection.