用AI生成假恶意软件数据，提升安卓病毒检测效果
Synthetic Data: AI's New Weapon Against Android Malware
- 用条件生成对抗网络生成仿真恶意软件数据
- 生成数据让分类器准确率提升，且可跨数据集通用
- 适合做安全研究或需要高质量训练数据的团队
随着安卓设备激增和恶意软件快速演化，2024年全球恶意软件样本已超3500万份，传统检测方法面临挑战。攻击者利用人工智能制造复杂变种，逃避现有防护。尽管机器学习在恶意软件分类中表现良好，但其性能高度依赖更新及时、质量高的数据集。真实恶意样本获取难、标注成本高，制约了模型发展。本文提出MalSynGen，一种基于条件生成对抗网络（cGAN）的恶意软件合成数据生成方法，可生成保持真实数据统计特性的表格型合成数据，有效提升安卓恶意软件分类器性能。通过多数据集与多项指标评估，验证了该方法在数据保真度、分类效用和计算效率上的优势。实验表明，MalSynGen具备跨数据集泛化能力，为解决恶意软件检测中数据过时与低质问题提供了可行方案。
原文摘要 · Abstract (English)
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.
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