A Survey of Machine Learning Methods and Challenges for Windows Malware Classification

• URL: http://arxiv.org/abs/2006.09271v2
• Date: Sun, 15 Nov 2020 16:35:36 GMT
• Title: A Survey of Machine Learning Methods and Challenges for Windows Malware Classification
• Authors: Edward Raff, Charles Nicholas
• Abstract summary: Survey aims to be useful both to cybersecurity practitioners who wish to learn more about how machine learning can be applied to the malware problem, and to give data scientists the necessary background into the challenges in this uniquely complicated space.
• Score: 43.4550536920809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Malware classification is a difficult problem, to which machine learning methods have been applied for decades. Yet progress has often been slow, in part due to a number of unique difficulties with the task that occur through all stages of the developing a machine learning system: data collection, labeling, feature creation and selection, model selection, and evaluation. In this survey we will review a number of the current methods and challenges related to malware classification, including data collection, feature extraction, and model construction, and evaluation. Our discussion will include thoughts on the constraints that must be considered for machine learning based solutions in this domain, and yet to be tackled problems for which machine learning could also provide a solution. This survey aims to be useful both to cybersecurity practitioners who wish to learn more about how machine learning can be applied to the malware problem, and to give data scientists the necessary background into the challenges in this uniquely complicated space.

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