Malware detection based on semi-supervised learning with malware visualization

Malware detection based on semi-supervised learning with malware visualization

The traditional signature-based detection method requires detailed manual analysis to extract the signatures of malicious samples, and requires a large number of manual markers to maintain the signature library, which brings a great time and resource costs, and makes it difficult to adapt to the rap...

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Bibliographic Details
Main Authors: Tan Gao, Lan Zhao, Xudong Li, Wen Chen
Format: Article
Language:English
Published: AIMS Press 2021-07-01
Series:Mathematical Biosciences and Engineering
Subjects:
malicious sample detection
collaborative learning
feature fusion
noise robustness
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2021300?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2021300?viewType=HTML

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