论文标题
生成对抗网络和基于图像的恶意软件分类
Generative Adversarial Networks and Image-Based Malware Classification
论文作者
论文摘要
为了有效删除恶意软件，确定恶意软件威胁水平和损害估计，恶意软件家庭分类起着至关重要的作用。在本文中，我们从恶意软件可执行文件中提取功能，并使用各种方法表示它们为图像。然后，我们专注于用于多类分类的生成对抗网络（GAN），并将我们的GAN结果与其他流行的机器学习技术进行比较，包括支持向量机（SVM），XGBoost和受限的Boltzmann机器（RBM）。我们发现，AC-GAN歧视者通常与其他机器学习技术竞争。我们还评估了对基于图像的恶意软件检测的对抗性攻击的GAN生成模型的实用性。尽管Ac-GAN生成的图像在视觉上令人印象深刻，但我们发现它们很容易使用几种学习技术中的任何一个与真实的恶意软件图像区分开来。该结果表明，我们的gan生成的图像在对抗攻击中几乎没有价值。
For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on Generative Adversarial Networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including Support Vector Machine (SVM), XGBoost, and Restricted Boltzmann Machines (RBM). We find that the AC-GAN discriminator is generally competitive with other machine learning techniques. We also evaluate the utility of the GAN generative model for adversarial attacks on image-based malware detection. While AC-GAN generated images are visually impressive, we find that they are easily distinguished from real malware images using any of several learning techniques. This result indicates that our GAN generated images would be of little value in adversarial attacks.
