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Related Experiment Video

Updated: Jul 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

PSO-DT based BagDT: a robust lightweight ensemble framework for efficient feature selection and DDoS attack detection

J Jasmine Shirley¹, M Priya²

¹School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India.

Scientific Reports

|October 16, 2025

Summary

A new PSO-DT-based BagDT ensemble model efficiently detects Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT). This lightweight model achieves high accuracy, making it ideal for resource-constrained smart environments.

Keywords:

DDoS attack Ensemble learning Feature selection Internet of things Particle swarm optimization

Related Experiment Videos

Last Updated: Jul 18, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Cybersecurity
Machine Learning
Internet of Things (IoT)

Background:

The Internet of Things (IoT) has expanded significantly, increasing vulnerability to cyber-threats like Distributed Denial of Service (DDoS) attacks.
Real-time detection of DDoS attacks is crucial for securing IoT environments and preventing disruption of critical services.
Existing deep learning models (CNNs, LSTMs) are often too computationally intensive for resource-constrained IoT devices.

Purpose of the Study:

To propose a robust and efficient hybrid framework for real-time DDoS attack detection in IoT environments.
To address the limitations of high computational overhead in deep learning models for IoT.
To develop a lightweight and scalable solution suitable for contemporary smart environments.

Main Methods:

Developed a hybrid framework using Particle Swarm Optimization (PSO) combined with Decision Trees (DT) for effective feature selection.
Evaluated the PSO-DT feature selection algorithm with ensemble learners: Random Subspace KNN, AdaBoost, RUSBoost, and Bagged Decision Trees (BagDT).
Focused on reducing computational cost and model size while maintaining high detection accuracy.

Main Results:

The proposed PSO-DT-based BagDT ensemble model achieved 99.96% accuracy and a macro-average precision, recall, and F1-score of 0.99.
Compared to other variants, the BagDT model showed a 4.13% increase in accuracy and a 95.49% reduction in training time.
Demonstrated a 63.52% increase in overall throughput, confirming the model's efficiency.

Conclusions:

The PSO-DT-based BagDT ensemble model offers a superior, efficient, and scalable solution for real-time DDoS attack detection in IoT.
The hybrid approach effectively reduces complexity and computational overhead, making it suitable for resource-constrained IoT devices.
The model's high performance validates its potential for implementation in modern smart environments.