@article {10.3844/jcssp.2025.146.157,
article_type = {journal},
title = {The Optimized Extreme Learning Machine (GA-OELM) for DDoS Attack Detection in Cloud Environment},
author = {Ec-Sabery, Meryem and Abbou, Adil Ben and Boushaba, Abdelali and Mrabti, Fatiha and Abbou, Rachid Ben},
volume = {21},
number = {1},
year = {2024},
month = {Dec},
pages = {146-157},
doi = {10.3844/jcssp.2025.146.157},
url = {https://thescipub.com/abstract/jcssp.2025.146.157},
abstract = {The widespread adoption of cloud computing has increased the attack surface and raised significant security concerns. A Distributed Denial of Service (DDoS) is a serious attack that depletes the network and server resources in cloud computing, causing service downtime or reduced performance. Therefore, defending against DDoS attacks becomes an urgent need. In this present paper, we propose an Optimized Extreme Learning Machine based on Genetic Algorithm (GA-OELM) for detecting DDoS attack patterns. The proposed model uses an improved GA for optimizing the weights and biases of the ELM hidden layer. The experiment is evaluated using three datasets namely, CICDDOS2019, NSL-KDD, and UNSW-NB15, and proves that the detection performance of the proposed GA-OELM is better than the classic ELM model and some state of art techniques.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}