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Introducing a hybrid intrusion detection method for IoT-cloud environments based on ResNeXt and improved Ebola

Juan Wu¹, Shuai Fu², Mohammad Sarabi^3,4

¹College of Computer and Big Data, Jining Normal College, Ulanqab, Inner Mogolia, 012000, China.

Scientific Reports

|October 29, 2025

Summary

This study introduces a hybrid intrusion detection system for IoT-cloud environments using ResNeXt and Improved Ebola Optimization Search Algorithm (IEOSA). The novel approach significantly enhances security by accurately identifying network attacks.

Keywords:

Cloud environments Cyberattacks Improved ebola optimization search algorithm ResNeXt The internet of things

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Area of Science:

Cybersecurity
Artificial Intelligence
Cloud Computing

Background:

The Internet of Things (IoT) and cloud computing integration creates significant vulnerabilities.
Increased connectivity expands the attack surface, necessitating advanced intrusion detection methods.

Purpose of the Study:

To develop a hybrid intrusion detection system for IoT-cloud environments.
To enhance the security and reliability of IoT-cloud ecosystems.

Main Methods:

Utilized ResNeXt, a deep convolutional neural network (DCNN) architecture, for efficient feature extraction.
Employed the Improved Ebola Optimization Search Algorithm (IEOSA), a novel metaheuristic optimizer, for enhanced search performance.

Main Results:

Achieved a detection accuracy of 98.3% on standard datasets (CICIDS 2017, NSL-KDD).
Secured over 97% for recall, F1 score, and precision, demonstrating superior intrusion detection capabilities.

Conclusions:

The hybrid framework effectively integrates deep learning with metaheuristic optimization for robust intrusion detection.
This approach offers a more secure and efficient IoT-cloud ecosystem compared to traditional methods.