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Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware

Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware

Winfred Yaokumah, Justice Kwame Appati, Daniel Kumah
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 18
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.286768
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MLA

Yaokumah, Winfred, et al. "Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware." IJCINI vol.15, no.4 2021: pp.1-18. http://doi.org/10.4018/IJCINI.286768

APA

Yaokumah, W., Appati, J. K., & Kumah, D. (2021). Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-18. http://doi.org/10.4018/IJCINI.286768

Chicago

Yaokumah, Winfred, Justice Kwame Appati, and Daniel Kumah. "Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-18. http://doi.org/10.4018/IJCINI.286768

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Abstract

This study aims to analyze the performance of machine learning models for detecting Internet of Things malware utilizing a recent IoT dataset. Experiments on the IoT dataset were conducted with nine well-known machine learning techniques, consisting of Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Neural Networks (NN), Random Forest (RF), Bagging (BG), and Stacking (ST). The results show that the proposed model attains 100% accuracy in detecting IoT malware for DT, SVM, RF, BG; about 99.9% percent for LR, NB, KNN, NN; and only 28.16% for ST classifier. This study also shows higher performance than other proposed machine learning models evaluated on the same dataset. Therefore, the results of this study can help both the researchers and application developers in designing and building intelligent malware detection systems for IoT devices.