The Optimized Extreme Learning Machine (GA-OELM) for DDoS Attack Detection in Cloud Environment
- 1 Department of Computer Science, Faculty of Sciences and Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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.
DOI: https://doi.org/10.3844/jcssp.2025.146.157
Copyright: © 2025 Meryem Ec-Sabery, Adil Ben Abbou, Abdelali Boushaba, Fatiha Mrabti and Rachid Ben Abbou. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- DDoS Attack
- Extreme Learning Machine
- Genetic Algorithm
- Cloud Computing