用图像化方法提升恶意软件检测准确率
A Visualized Malware Detection Framework with CNN and Conditional GAN
- 将恶意代码转为黑白像素图保留原始特征
- 用条件GAN生成图像解决样本不平衡问题
- 结合CNN实现97%以上检测准确率，适合安全研究者
将恶意软件可视化分析与机器学习结合已被证明是提升多平台安全防御的有前景方案。本文提出一个集成框架，解决机器学习在开发恶意软件检测系统时常遇问题。设计一种图像呈现系统，通过将每个变量编码为二进制并映射为黑白像素，保留良性/恶意样本的身份特征。采用基于条件生成对抗网络（Conditional GAN）的模型生成合成图像，缓解类别不平衡问题。使用卷积神经网络构建检测模型，在含与不含人工生成样本的数据集上进行训练。结果表明，两种训练场景下的准确率分别为98.51%和97.26%。
原文摘要 · Abstract (English)
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.
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