论文标题
深度学习和开放式恶意软件分类：调查
Deep Learning and Open Set Malware Classification: A Survey
论文作者
论文摘要
随着互联网这些年的迅速增长，通常称为恶意软件的恶意软件的变体已成为对互联网用户的主要且严重的威胁之一。恶意软件的急剧增加导致了一个研究领域，不仅使用尖端机器学习技术将恶意软件分类为已知家庭，而且还识别未知的家庭，这可能与机器学习中的开放式识别（OSR）问题有关。最近的机器学习作品从不同的场景中阐明了开放式识别（OSR）。在缺失未知培训样本的情况下，OSR系统不仅应正确对已知类别进行分类，还应识别未知类别。这项调查概述了不同的深度学习技术，对OSR和图形表示解决方案的讨论以及恶意软件分类系统的介绍。
As the Internet is growing rapidly these years, the variant of malicious software, which often referred to as malware, has become one of the major and serious threats to Internet users. The dramatic increase of malware has led to a research area of not only using cutting edge machine learning techniques classify malware into their known families, moreover, recognize the unknown ones, which can be related to Open Set Recognition (OSR) problem in machine learning. Recent machine learning works have shed light on Open Set Recognition (OSR) from different scenarios. Under the situation of missing unknown training samples, the OSR system should not only correctly classify the known classes, but also recognize the unknown class. This survey provides an overview of different deep learning techniques, a discussion of OSR and graph representation solutions and an introduction of malware classification systems.
