TY  - CONF
ID  - SisLab4787
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4787/
A1  - Hoang, Trong-Minh
A1  - Tran, Nhat-Hoang
A1  - Thai, Vu-Long
A1  - Nguyen, Dinh-Long
A1  - Nguyen, Nam-Hoang
Y1  - 2022/10//
N2  - The growing Internet of Things (IoT) applications of today have brought numerous benefits to our lives. In addition, cyber-attacks are growing as a result of increasingly sophisticated and violent attacks. Detection systems that serve as security protection against emerging attacks are also being developed using machine learning techniques.
However, many additional challenges continue to emerge as
demand for Intrusion Detection System (IDS) deployment at the edge network, where resource-constrained devices exist,
continues to increase. These devices require a database with a high level of accuracy for attack detection. This research
provides a Fuzzy-based IDS for detecting DDOS attacks with
over 99 percent accuracy rate that is deployable on edge
computing using the IoT23 dataset.
TI  - An Efficient IDS Using FIS to Detect DDoS in IoT Networks
SP  - 1
AV  - none
EP  - 6
T2  - NAFOSTED Conference on Information and Computer Science (NICS)
ER  -