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Design and Analysis for Fall Detection System Simplification

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Using machine learning algorithms to enhance IoT system security.

Hosam El-Sofany¹, Samir A El-Seoud², Omar H Karam²

¹College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia. helsofany@kku.edu.sa.

Scientific Reports

|May 27, 2024

Summary

This study introduces a machine learning model to enhance Internet of Things (IoT) security. The novel approach achieves 99.9% accuracy in detecting cyberattacks on IoT devices.

Keywords:

Internet of Things IoT security Machine learning Sustainable cities and communities Sustainable development goals

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Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

The Internet of Things (IoT) connects numerous devices, expanding its applications across various sectors.
Increasing IoT device proliferation escalates security vulnerabilities and risks.
Existing security measures struggle to autonomously manage the growing threat landscape.

Purpose of the Study:

To propose a novel machine learning (ML)-based model for enhancing the security of Internet of Things (IoT) systems.
To analyze current ML technologies, security solutions, and vulnerabilities in intelligent IoT systems.
To develop an autonomous security model capable of managing evolving IoT security challenges.

Main Methods:

The study reviewed recent technologies, security strategies, and intelligent solutions for ML-based IoT systems.
Seven ML algorithms were evaluated to identify optimal classifiers for an AI-based reaction agent.
A novel ML-based security model was developed for autonomous cyberattack detection in IoT networks.

Main Results:

The proposed ML model achieved 99.9% accuracy, 99.8% detection average, and a 99.9 F1 score.
A perfect AUC score of 1 was obtained, indicating superior performance in cyberattack detection.
The approach demonstrated enhanced execution speed and accuracy compared to previous ML-based models.

Conclusions:

The developed ML-based security model effectively enhances IoT security by autonomously detecting cyberattacks.
The proposed solution offers a significant advancement in securing IoT environments against emerging threats.
This research provides a highly accurate and efficient method for identifying attack patterns in IoT networks.