ConvProtoNet: Deep Prototype Induction towards Better Class Representation for Few-Shot Malware Classification

ConvProtoNet: Deep Prototype Induction towards Better Class Representation for Few-Shot Malware Classification

Traditional malware classification relies on known malware types and significantly large datasets labeled manually which limits its ability to recognize new malware classes. For unknown malware types or new variants of existing malware containing only a few samples each class, common classification...

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Bibliographic Details
Main Authors: Zhijie Tang, Peng Wang, Junfeng Wang
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Applied Sciences
Subjects:
few-shot learning
malware classification
meta-learning
prototype induction
Online Access:https://www.mdpi.com/2076-3417/10/8/2847

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https://www.mdpi.com/2076-3417/10/8/2847

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