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Decision Trees Unleashed: Simplifying IoT Malware Detection With Advanced AI Techniques

Decision Trees Unleashed: Simplifying IoT Malware Detection With Advanced AI Techniques

Derek Mohammed, Marwan Omar
Copyright: © 2024 |Pages: 19
ISBN13: 9798369319062|EISBN13: 9798369319079
DOI: 10.4018/979-8-3693-1906-2.ch013
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MLA

Mohammed, Derek, and Marwan Omar. "Decision Trees Unleashed: Simplifying IoT Malware Detection With Advanced AI Techniques." Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology, edited by Darrell Norman Burrell, IGI Global, 2024, pp. 240-258. https://doi.org/10.4018/979-8-3693-1906-2.ch013

APA

Mohammed, D. & Omar, M. (2024). Decision Trees Unleashed: Simplifying IoT Malware Detection With Advanced AI Techniques. In D. Burrell (Ed.), Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology (pp. 240-258). IGI Global. https://doi.org/10.4018/979-8-3693-1906-2.ch013

Chicago

Mohammed, Derek, and Marwan Omar. "Decision Trees Unleashed: Simplifying IoT Malware Detection With Advanced AI Techniques." In Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology, edited by Darrell Norman Burrell, 240-258. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1906-2.ch013

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Abstract

This chapter presents an in-depth study on the application of decision tree-based classifiers for the detection of malware in internet of things (IoT) environments. With the burgeoning expansion of IoT devices, the threat landscape has grown increasingly complex, making traditional security measures insufficient. This study proposes an innovative approach using decision tree algorithms to address the growing concern of IoT malware. The research methodology encompasses a comprehensive analysis of IoT vulnerabilities, focusing on malware threats and the development of a decision tree-based classifier. The classifier is empirically validated using the MaleVis dataset, a rich source of real-world IoT malware data. Performance metrics such as precision, recall, specificity, F1-score, accuracy, and processing time are meticulously evaluated to determine the efficacy of the model.

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