Using machine learning algorithms to enhance IoT system security.

Autor: El-Sofany H; College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia. helsofany@kku.edu.sa., El-Seoud SA; Faculty of Informatics and Computer Science, British University in Egypt-BUE, Cairo, Egypt., Karam OH; Faculty of Informatics and Computer Science, British University in Egypt-BUE, Cairo, Egypt., Bouallegue B; College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia.; Electronics and Micro-Electronics Laboratory (E. μ. E. L), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 27; Vol. 14 (1), pp. 12077. Date of Electronic Publication: 2024 May 27.
DOI: 10.1038/s41598-024-62861-y
Abstrakt: The term "Internet of Things" (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent's implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent's implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.
(© 2024. The Author(s).)
Databáze: MEDLINE
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