FedDB: A Federated Learning Approach Using DBSCAN for DDoS Attack Detection
The rise of Distributed Denial of Service (DDoS) attacks on the internet has necessitated the development of robust and efficient detection mechanisms. DDoS attacks continue to present a significant threat, making it imperative to find efficient ways to detect and prevent these attacks promptly. Tra...
Saved in:
| Main Authors: | Yi-Chen Lee, Wei-Che Chien, Yao-Chung Chang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/22/10236 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach
by: Ahmad Almadhor, et al.
Published: (2024-10-01) -
Federated Learning for Decentralized DDoS Attack Detection in IoT Networks
by: Yaser Alhasawi, et al.
Published: (2024-01-01) -
DDoS Attack Detection in IoT: A Comparative Resource and Performance Analysis of Deep Learning and Machine Learning Models
by: Amer Abualhassan, et al.
Published: (2025-01-01) -
RF-RFE-SMOTE: A DoS And DDoS Attack Detection Framework
by: Nora Rashid Najam, et al.
Published: (2023-10-01) -
Deep Ensemble Learning With Pruning for DDoS Attack Detection in IoT Networks
by: Makhduma F. Saiyedand, et al.
Published: (2024-01-01)