With the rapid growth of the number of devices on the Internet, malware poses a threat not only to the affected devices but also their ability to use said devices to launch attacks on the Internet ecosystem. Rapid malware classification is an important tools to combat that threat. One of the successful approaches to classification is based on malware images and deep learning. While many deep learning architectures are very accurate they usually take a long time to train. In this work we perform experiments on multiple well known, pre-trained, deep network architectures in the context of transfer learning. We show that almost all them classify malware accurately with a very short training period.

中文翻译：

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使用迁移学习进行恶意软件分类

随着 Internet 上设备数量的快速增长，恶意软件不仅对受影响的设备构成威胁，而且对它们使用这些设备对 Internet 生态系统发起攻击的能力构成威胁。快速恶意软件分类是对抗该威胁的重要工具。一种成功的分类方法是基于恶意软件图像和深度学习。虽然许多深度学习架构非常准确，但它们通常需要很长时间来训练。在这项工作中，我们在迁移学习的背景下对多个众所周知的、预训练的、深度网络架构进行了实验。我们表明，几乎所有人都在非常短的训练时间内准确地对恶意软件进行了分类。