Deep Learning and Open Set Malware Classification: A Survey
April 08, 2020 ยท The Cartographer ยท ๐ arXiv.org
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"Title-pattern auto-detect: Deep Learning and Open Set Malware Classification: A Survey"
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Authors
Jingyun Jia
arXiv ID
2004.04272
Category
cs.LG: Machine Learning
Cross-listed
cs.CR,
stat.ML
Citations
2
Venue
arXiv.org
Last Checked
4 days ago
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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