Deep Learning and Open Set Malware Classification: A Survey

• URL: http://arxiv.org/abs/2004.04272v1
• Date: Wed, 8 Apr 2020 21:36:21 GMT
• Title: Deep Learning and Open Set Malware Classification: A Survey
• Authors: Jingyun Jia
• Abstract summary: Recent machine learning works have shed light on Open Set Recognition (OSR) problem in machine learning. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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