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Lightweight machine learning framework for efficient DDoS attack detection in IoT networks.

Mamoona Nawaz¹, Shireen Tahira¹, Dilawar Shah²

¹Department of Computer Science, International Islamic University, Islamabad, Pakistan.

Scientific Reports

|July 10, 2025

Summary

This summary is machine-generated.

This study presents a lightweight machine learning framework for detecting Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks. The Random Forest model achieved over 99% accuracy, offering an efficient IoT security solution.

Keywords:

Artificial intelligence Cybersecurity DDoS attacks Internet of things Machine learning Networking

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Published on: September 8, 2023

Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Internet of Things (IoT) devices face significant security threats from Distributed Denial of Service (DDoS) attacks.
Traditional DDoS detection methods are too computationally intensive for resource-constrained IoT environments.

Purpose of the Study:

To propose a lightweight and scalable machine learning-based DDoS detection framework for IoT networks.
To evaluate the performance of different machine learning models for DDoS detection in IoT.

Main Methods:

Utilized the NSL-KDD dataset for training and evaluation.
Employed Extra Trees Classifier (ETC) for feature selection to reduce dimensionality.
Implemented and compared Random Forest, Logistic Regression, and Naïve Bayes models.

Main Results:

The Random Forest model achieved high performance metrics: 99.88% accuracy, 99.93% precision, 99.81% recall, and 99.87% F1-score.
The proposed framework significantly reduces computational overhead compared to deep learning methods.
Random Forest outperformed Logistic Regression (91.61% accuracy) and Naïve Bayes (87.62% accuracy).

Conclusions:

The developed framework provides an efficient, scalable, and accurate solution for DDoS attack detection in IoT.
This research addresses the challenge of balancing high performance with resource limitations in IoT security.
The findings contribute to advancing the security of real-world IoT applications.