Deep Learning and Open Set Malware Classification: A Survey
It tackles the threat of malware for Internet users by reviewing incremental advancements in classification systems.
This survey addresses the problem of classifying malware into known families and recognizing unknown variants using deep learning and open set recognition techniques, providing an overview of existing methods and solutions.
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.