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Harnessing the Power and Simplicity of Decision Trees to Detect IoT Malware

Harnessing the Power and Simplicity of Decision Trees to Detect IoT Malware

Marwan Omar, Rebet Jones, Darrell Norman Burrell, Maurice Dawson, Calvin Nobles, Derek Mohammed, Ali Kashif Bashir
Copyright: © 2023 |Pages: 15
ISBN13: 9798369316344|ISBN13 Softcover: 9798369348604|EISBN13: 9798369316351
DOI: 10.4018/979-8-3693-1634-4.ch013
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MLA

Omar, Marwan, et al. "Harnessing the Power and Simplicity of Decision Trees to Detect IoT Malware." Transformational Interventions for Business, Technology, and Healthcare, edited by Darrell Norman Burrell, IGI Global, 2023, pp. 215-229. https://doi.org/10.4018/979-8-3693-1634-4.ch013

APA

Omar, M., Jones, R., Burrell, D. N., Dawson, M., Nobles, C., Mohammed, D., & Bashir, A. K. (2023). Harnessing the Power and Simplicity of Decision Trees to Detect IoT Malware. In D. Burrell (Ed.), Transformational Interventions for Business, Technology, and Healthcare (pp. 215-229). IGI Global. https://doi.org/10.4018/979-8-3693-1634-4.ch013

Chicago

Omar, Marwan, et al. "Harnessing the Power and Simplicity of Decision Trees to Detect IoT Malware." In Transformational Interventions for Business, Technology, and Healthcare, edited by Darrell Norman Burrell, 215-229. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/979-8-3693-1634-4.ch013

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Abstract

Due to its simple installation and connectivity, the internet of things (IoT) is susceptible to malware attacks. As IoT devices have become more prevalent, they have become the most tempting targets for malware. In this chapter, the authors propose a novel detection and analysis method that harnesses the power and simplicity of decision trees. The experiments are conducted using a real word dataset, MaleVis, which is a publicly available dataset. Based on the results, the authors show that this proposed approach outperforms existing state-of-the-art solutions in that it achieves 97.23% precision and 95.89% recall in terms of detection and classification. A specificity of 96.58%, F1-score of 96.40%, an accuracy of 96.43%, and an average processing time per malware classification of 789 ms.

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