TY  - JOUR
AU  - Ec-Sabery, Meryem 
AU  - Abbou, Adil Ben 
AU  - Boushaba, Abdelali 
AU  - Mrabti, Fatiha 
AU  - Abbou, Rachid Ben 
PY  - 2024
TI  - The Optimized Extreme Learning Machine (GA-OELM) for DDoS Attack Detection in Cloud Environment
JF  - Journal of Computer Science
VL  - 21
IS  - 1
DO  - 10.3844/jcssp.2025.146.157
UR  - https://thescipub.com/abstract/jcssp.2025.146.157
AB  - 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.