Advanced memory forensics for malware classification with deep learning algorithms

The growing complexity of malware, especially polymorphic and obfuscated variants, has exposed significant limitations in traditional detection methods. This study addresses these challenges using memory forensics to detect and classify malware through deep learning algorithms. Memory-based features...

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Bibliographic Details
Published in:	Cluster computing Vol. 28; no. 6; p. 353
Main Authors:	Odeh, Ammar, Taleb, Anas Abu, Alhajahjeh, Tareq, Navarro, Francisco
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Artificial neural networks Behavior Classification Computer Communication Networks Computer Science Cybersecurity Deep learning Feature extraction Feature selection Forensic computing Forensic sciences Machine learning Malware Methods Operating Systems Processor Architectures Recurrent neural networks Security systems Threats Deep learning Recurrent neural networks Autoencoders Memory forensics Feature extraction Convolutional neural networks Malware detection
ISSN:	1386-7857, 1573-7543
Online Access:	Get full text
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