Classification of Malware in IoT Devices Based on the Random Forest Algorithm

Nirmin Monir, Walid Elsayed, A. A. Shaalan, Mohamed A. Seifeldin, Shaymaa A. Hassan

Classification of Malware in IoT Devices Based on the Random Forest Algorithm

Authors

Nirmin Monir, Walid Elsayed, A. A. Shaalan, Mohamed A. Seifeldin, Shaymaa A. Hassan

Keywords:

IoT, Machine Learning, Malware Detection, Random Forest, CICIOT2023 Dataset

Abstract

The participation of ordinary devices in networking has created a world of connected devices rapidly. The Internetof Things (IoT) includes heterogeneous devices from every field. There are no definite protocols or standards for IoTcommunication, and most of the IoT devices have limited resources. Enabling a complete security measure for suchdevices is a challenging task

References

Alrubayyi, H., Goteng, G., Jaber, M., & Kelly, J. (2021). Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches. Journal of Sensor and Actuator Networks, 10(4), 61. https://doi.org/10.3390/jsan10040061

Riaz, S., Latif, S., Usman, S. M., Ullah, S. S., Algarni, A. D., Yasin, A., Anwar, A., Elmannai, H., & Hussain, S. (2022). Malware Detection in Internet of Things (IoT) Devices Using Deep Learning. Sensors, 22(23), 9305. https://doi.org/10.3390/s22239305

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Published

2024-06-20

How to Cite

Nirmin Monir, Walid Elsayed, A. A. Shaalan, Mohamed A. Seifeldin, Shaymaa A. Hassan. (2024). Classification of Malware in IoT Devices Based on the Random Forest Algorithm . Journal of Computational Analysis and Applications (JoCAAA), 33(06), 2515–2528. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3436