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: | , , |
|---|---|
| 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!
|
| _version_ | 1850138107594145792 |
|---|---|
| author | Nelly Elsayed Zag ElSayed Ahmed Abdelgawad |
| author_facet | Nelly Elsayed Zag ElSayed Ahmed Abdelgawad |
| author_sort | Nelly Elsayed |
| collection | DOAJ |
| description |
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 monitoring, and firewalls. However, complex DoS and DDoS attacks can bypass these detection mechanisms. Thus, this paper proposes utilizing convolutional neural network-based transfer learning to detect DoS and DDoS attacks from converted network traffic data into images. We employed the Xception model with fine-tuning, and we achieved
an average of 91% accuracy in detecting eleven different types of DoS and DDoS attacks, which is higher than the current state-of-the-art by 5% targeting the same task.
|
| format | Article |
| id | doaj-art-dcee079af4e842ad80cfbe83b9df0eac |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-dcee079af4e842ad80cfbe83b9df0eac2025-08-20T02:30:39ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.138690DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with ExplainabilityNelly Elsayed0https://orcid.org/0000-0003-0082-1450Zag ElSayed1https://orcid.org/0000-0001-9094-1469Ahmed Abdelgawad2https://orcid.org/0000-0002-6655-2065University of CincinnatiUniversity of CincinnatiCentral Michigan University 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 monitoring, and firewalls. However, complex DoS and DDoS attacks can bypass these detection mechanisms. Thus, this paper proposes utilizing convolutional neural network-based transfer learning to detect DoS and DDoS attacks from converted network traffic data into images. We employed the Xception model with fine-tuning, and we achieved an average of 91% accuracy in detecting eleven different types of DoS and DDoS attacks, which is higher than the current state-of-the-art by 5% targeting the same task. https://journals.flvc.org/FLAIRS/article/view/138690Cybersecuritydeep learningexplainabilityPretrained models |
| spellingShingle | Nelly Elsayed Zag ElSayed Ahmed Abdelgawad DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability Proceedings of the International Florida Artificial Intelligence Research Society Conference Cybersecurity deep learning explainability Pretrained models |
| title | DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability |
| title_full | DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability |
| title_fullStr | DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability |
| title_full_unstemmed | DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability |
| title_short | DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability |
| title_sort | dos and ddos attack detection in iot infrastructure using xception model with explainability |
| topic | Cybersecurity deep learning explainability Pretrained models |
| url | https://journals.flvc.org/FLAIRS/article/view/138690 |
| work_keys_str_mv | AT nellyelsayed dosandddosattackdetectioniniotinfrastructureusingxceptionmodelwithexplainability AT zagelsayed dosandddosattackdetectioniniotinfrastructureusingxceptionmodelwithexplainability AT ahmedabdelgawad dosandddosattackdetectioniniotinfrastructureusingxceptionmodelwithexplainability |