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.

翻译：安卓设备数量的持续增长以及恶意软件的加速演进（预计到2024年样本数将超过3500万），凸显了有效检测方法的至关重要性。攻击者目前正利用人工智能创建复杂的恶意软件变体，这些变体能够轻易规避传统检测技术。尽管机器学习在恶意软件分类中展现出潜力，但其成功很大程度上依赖于最新、高质量数据集的可用性。获取和标注真实恶意软件样本的稀缺性与高成本，对开发鲁棒的检测模型构成了重大挑战。本文提出MalSynGen，一种恶意软件合成数据生成方法，该方法采用条件生成对抗网络（cGAN）来生成合成表格数据。该数据保留了真实世界数据的统计特性，并提升了安卓恶意软件分类器的性能。我们使用多种数据集和评估指标对该方法的有效性进行了验证，这些指标评估了生成数据的保真度、其在分类中的效用以及过程的计算效率。实验结果表明，MalSynGen能够泛化至不同数据集，为解决恶意软件检测中数据过时和低质量的问题提供了可行方案。