The advancement in recent IoT devices has led to catastrophic attacks on the devices resulting in breaches in user privacy and exhausting resources of various organizations, so that users and organizations expend increased time and money. One such harmful malware is Mirai, which has created worldwide recognition by impacting the digital world. There are several ways to detect Mirai, but the Machine Learning approach has proved to be accurate and reliable in detecting malware. In this research, a novel-based approach of detecting Mirai using Machine Learning Algorithm is proposed and implemented in Matlab and Python. To evaluate the proposed approaches, Mirai and Benign datasets are considered and training is performed on the dataset comprised of a Training set, Cross-Validation set and Test set using Artificial Neural Network (ANN) consisting of neurons in the hidden layer, which provides consistent accuracy, precision, recall and F-1 score. In this research, an accurate number of hidden layers and neurons are chosen to avoid the problem of Overfitting. This research provides a comparative analysis between ANN and Random Forest models of the dataset formed by merging Mirai and benign datasets of the Mirai malware detection pertaining to seven IoT devices. The dataset used in this research is “N-BaIoT” dataset, which represents data in the features infected by Mirai Malware. The results are found to be accurate and reliable as the best performance was achieved with an accuracy of 92.8% and False Negative rate of 0.3% and F-1 score of 0.99. The expected outcomes of this project, include major findings towards cost-effective Learning solutions in detecting Mirai Malware strains.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.