Affiliation:
1. Capitol Technology University, USA & Saint Leo University, USA
2. Capitol Technology University, USA & Illinois Institute of Technology, USA
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.
Reference82 articles.
1. Efficient Security and Privacy of Lossless Secure Communication for Sensor-based Urban Cities
2. Millimeter-wave channel modeling in a VANETs using coding techniques
3. Al HarthiM. A. S.Al BalushiM. M. Y.Al BadiM. A. H.Al KarakiJ.OmarM. (n.d.). Metaverse Adoption in UAE Higher Education: A Hybrid SEM-ANN Approach.......... 98 Mohammad Daradkeh. Boshra Aldhanhani, Amjad Gawanmeh, Shadi Atalla and Sami Miniaoui.
4. Al-KarakiJ. N.OmarM.GawanmehA.JonesA. (2023). Advancing CyberSecurity Education and Training: Practical Case Study of Running Capture the Flag (CTF) on the Metaverse vs. Physical Settings. IEEE.
5. Al KinoonM.OmarM.MohaisenM.MohaisenD. (2021). Security breaches in the healthcare domain: a spatiotemporal analysis. Springer International Publishing.