10th International Congress on Information and Communication Technology

Authors - Ahmed Abu-Khadrah, Munirah Ali ALMutairi, Mohammad R. Hassan, Ali Mohd Ali
Abstract - The Internet of Things (IoT) devices are employed in various industries, including health care, smart homes, smart grids, and smart cities. Researchers address the intricate connection between the growth of the Internet of Things and the hazards to its security. The vast and varied features of the Internet of Things make traditional security solutions ineffective. A new model is developed to enhance IoT malware detection by combining three machine learning algorithms: KNN, Bagging, and support vector machines. The proposed model is evaluated by measuring accuracy, precision, recall and F1-score. In addition, two comprehensive datasets are utilized to evaluate the proposed model dataset. The study explores the potential of three ensemble classification models for Malware Detection. This study investigated the efficacy of a novel ensemble machine-learning approach for detecting malware within the Internet of Things (IoT) domain. The result of this research is that the accuracy on the validation set is 95.76%, the precision on the validation set is 97.01%, the recall is 94.55%, and the F1 score is 95.77%. The findings of this study indicate that the proposed model, a synergistic combination of K-Nearest Neighbours (KNN), Bagging, and Support Vector Machines (SVM), achieved a commendable overall accuracy of 95.76% in correctly classifying both malware and benign programs within the utilized IoT dataset.