Federated Learning for Decentralized DDoS Attack Detection in IoT Networks
In the ever-expanding domain of Internet of Things (IoT) networks, Distributed Denial of Service (DDoS) attacks represent a significant challenge, compromising the reliability of these systems. Traditional centralized detection methods struggle to cope effectively in the widespread and diverse envir...
Saved in:
Main Authors: | Yaser Alhasawi, Salem Alghamdi |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10474358/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data
by: Arjun Kumar Bose Arnob, et al.
Published: (2025-02-01) -
TCN-Based DDoS Detection and Mitigation in 5G Healthcare-IoT: A Frequency Monitoring and Dynamic Threshold Approach
by: Mirza Akhi, et al.
Published: (2025-01-01) -
Comprehensive Analysis of DDoS Anomaly Detection in Software-Defined Networks
by: Abdinasir Hirsi, et al.
Published: (2025-01-01) -
A Metaheuristic Approach to Detecting and Mitigating DDoS Attacks in Blockchain-Integrated Deep Learning Models for IoT Applications
by: Manal Alkhammash
Published: (2024-01-01) -
Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
by: Marcos Luengo Viñuela, et al.
Published: (2024-12-01)