article · Scientific Reports
The expanding use of connected Internet of Things devices across critical infrastructure, healthcare, transportation, and smart homes has heightened security risks. This research evaluates the benefits and limitations of using machine learning to protect these environments, addressing vulnerabilities through an autonomous security model. Seven different machine learning algorithms were tested to select the most accurate classifiers for an artificial intelligence reaction agent designed to detect cyberattack patterns within connected networks. The resulting detection model achieved high performance metrics, delivering 99.9 percent accuracy, a 99.8 percent detection average, an F1 score of 99.9, and a perfect area under the curve score of 1. By executing faster and more accurately than earlier approaches, the model demonstrates an effective method for identifying malicious network activity across diverse Internet of Things ecosystems without requiring constant human intervention.
Internet of Things devices are embedded in hospitals, transport systems, and homes, yet their expanding presence creates serious vulnerabilities to cyberattacks. Using automated machine learning models to identify attacks quickly and accurately helps protect critical connected systems without requiring continuous manual oversight, ensuring that malicious network activity can be caught before causing widespread disruption.
The model provides an automated cyberattack detection tool for organisations operating connected devices in areas such as healthcare, transport, smart cities, and critical infrastructure. As an applied and tested algorithmic model that identifies network threats with high accuracy and improved execution speed, it represents a software component suitable for integration into network security platforms, though the abstract does not describe field deployment or commercial testing.
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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.
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DOI: 10.1038/s41598-024-62861-y
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