Evaluation of machine learning algorithms for IoT malware detection in the IoT-23 dataset

• Cristian H. M. Souza CEETEPS
• Carlos H. Arima CEETEPS

DOI: https://doi.org/10.5753/sbseg.2024.241472

Abstract

This article presents an evaluation of different machine learning algorithms for malware detection in IoT devices using the IoT-23 dataset. Models based on Random Forest, SVM, decision tree, and a convolutional neural network were implemented and compared. The results show that the Random Forest algorithm achieved the highest accuracy, while the convolutional neural network and Random Forest obtained the best precision and F1-Score metrics. The data preprocessing methodology and evaluation metrics are detailed, providing a comprehensive overview of the models’ effectiveness and guiding future research.

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