DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability
The denial of service (DoS) and distributed denial of service (DDoS) attacks are considered the most frequent attacks targeting the Internet of Things (IoT) network infrastructure globally. The current approaches for detecting DoS and DDoS attacks mainly use intrusion detection systems, traffic mon...
Saved in:
| Main Authors: | Nelly Elsayed, Zag ElSayed, Ahmed Abdelgawad |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2025-05-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/138690 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
CryptoDNA: A Machine Learning Paradigm for DDoS Detection in Healthcare IoT, Inspired by Cryptojacking prevention Models
by: Zag ElSayed, et al.
Published: (2025-05-01) -
Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks
by: Sharaf Aldeen Abdulkadhum Abbas, et al.
Published: (2024-06-01) -
Lightweight machine learning framework for efficient DDoS attack detection in IoT networks
by: Mamoona Nawaz, et al.
Published: (2025-07-01) -
Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
by: Marcos Luengo Viñuela, et al.
Published: (2024-12-01) -
Intelligent Prevention of DDoS Attacks using Reinforcement Learning and Smart Contracts
by: Emily Struble, et al.
Published: (2024-05-01)