Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models

Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models

In the evolving cyber threat landscape, one of the most visible and pernicious challenges is malware activity detection and analysis. Traditional detection and analysis methods face threats of data high-dimensionality, lack of strength against adversarial attacks, and non-efficient use of unlabeled...

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Bibliographic Details
Main Authors: Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Autoencoders
gradient boosted decision trees
adversarial training
malware analysis
machine learning techniques
Online Access:https://ieeexplore.ieee.org/document/10757384/
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