An Adversarial Attack on ML-Based IoT Malware Detection Using Binary Diversification Techniques

An Adversarial Attack on ML-Based IoT Malware Detection Using Binary Diversification Techniques

The integration of machine learning (ML) has revolutionized malware detection, enabling accurate identification of subtle distinctions between malware and benignware. As the threat landscape continually evolves and new malware strains emerge, conventional signature-based detectors are becoming incre...

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Bibliographic Details
Main Authors: Maina Bernard Mwangi, Shin-Ming Cheng
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Adversarial attack
binary diversification
Internet of Things (IoT) malware detection
machine learning
Online Access:https://ieeexplore.ieee.org/document/10786223/
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https://ieeexplore.ieee.org/document/10786223/

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