An Evaluation of Real-time Malware Detection in IoT Devices: Comparison of Machine Learning Algorithms with RapidMiner

Abstract

In recent years, there has been a significant increase in malware attacks on IoT devices. As a result, there is a critical need to develop a robust malware detection model that can detect malware in real-time. This study explores different algorithms to identify the distinctions between various types of malware and develop a malware detection system based on botnets such as Mirai, Okiru, and Torii. We evaluate the performance of the malware detection system using RapidMiner and compare the results of different algorithms including Random Forest, Deep Learning, Naive Bayes, kNN, and Decision Tree. Our results show that the Random Forest algorithm outperforms the others and is the most effective at detecting malware in real-time.