Enhancing Malware Detection in IoT Networks using Ensemble Learning on IoT-23 Dataset

The Internet of Things (IoT) has become a technological innovation that brings many benefits in various sectors, but also presents challenges, especially in terms of cybersecurity. One of the main threats is malware, which can damage devices, steal data, and disrupt system performance. With the increasing use of IoT, malware attacks on IoT devices are a serious concern. Previous research shows that malware detection models in IoT devices still have shortcomings, especially in terms of accuracy. One of the algorithms used in malware detection, Naïve Bayes, has been shown to provide low accuracy results. This study aims to improve the accuracy of malware detection on IoT networks by applying Ensemble learning techniques using traffic data from the IoT-23 dataset. The methodology used refers to the CRISP-DM (Cross Industry Standard Process for Data Mining) framework, which includes the stages of domain understanding, data understanding, data preparation, modelling, evaluation, and deployment. The results show that Ensemble learning improved the performance of individual models. Naïve Bayes as a single model produces an accuracy of 0.24, increasing to 0.35 when combined with AdaBoost, and 0.99 when combined with XGBoost. The combination of the three models also produced an accuracy of 0.99. These results demonstrate the effectiveness of ensemble learning in improving malware detection accuracy in IoT environments.