Machine learning techniques for imbalanced multiclass malware classification through adaptive feature selection

Machine learning techniques for imbalanced multiclass malware classification through adaptive feature selection

Detecting polymorphic or metamorphic variants of known malware is an ever-growing challenge, just like detecting new malware. Artificial intelligence techniques are preferred over conventional signature-based malware detection as the number of malware variants proliferates. This article proposes an...

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Bibliographic Details
Main Authors: Binayak Panda, Sudhanshu Shekhar Bisoyi, Sidhanta Panigrahy, Prithviraj Mohanty
Format: Article
Language:English
Published: PeerJ Inc. 2025-03-01
Series:PeerJ Computer Science
Subjects:
Greedy feature selection
TF-IDF
Skip-gram
Machine learning
API sequence
Multiclass malware classification
Online Access:https://peerj.com/articles/cs-2752.pdf
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