Related Experiment Video
Updated: Jul 18, 2026
03:31
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
1.0K
PSO-DT based BagDT: a robust lightweight ensemble framework for efficient feature selection and DDoS attack detection
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.
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.
Related Concept Videos
01:20Parallel Processing
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
01:25Rapid Identification of Pathogens
MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...