A Novel Detection and Multi-Classification Approach for IoT-Malware Using Random Forest Voting of Fine-Tuning Convolutional Neural Networks

A Novel Detection and Multi-Classification Approach for IoT-Malware Using Random Forest Voting of Fine-Tuning Convolutional Neural Networks

The Internet of Things (IoT) is prone to malware assaults due to its simple installation and autonomous operating qualities. IoT devices have become the most tempting targets of malware due to well-known vulnerabilities such as weak, guessable, or hard-coded passwords, a lack of secure update proced...

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Bibliographic Details
Main Authors: Safa Ben Atitallah, Maha Driss, Iman Almomani
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Sensors
Subjects:
IoT-malware
detection
multi-classification
transfer learning
ensembling strategies
CNNs
Online Access:https://www.mdpi.com/1424-8220/22/11/4302

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https://www.mdpi.com/1424-8220/22/11/4302

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