Publication: Collaborative-Based Approach Utilizing Ensemble Feature Selection For Detecting Http-Get Ddos Attacks In Cloud Computing Environments
| dc.contributor.author | Ashhab, Ziyad Reefat Hamzeh Al | |
| dc.date.accessioned | 2026-06-25T06:52:27Z | |
| dc.date.available | 2026-06-25T06:52:27Z | |
| dc.date.issued | 2025-05 | |
| dc.description.abstract | Cloud computing environment (cce)-based services present a novel paradigm for remote business management. One of the primary advantages of utilizing cce is the availability of on-demand services, thereby facilitating a pay-per-use model. This makes cce technology a convenient means of facilitating services over the internet. However, security vulnerabilities, such as distributed denial of service (ddos) attacks, particularly http-get ddos attacks at the application layer, pose a significant threat to service availability in cces. This thesis proposes a collaborative approach utilizing ensemble feature selection to detect http-get ddos attacks in cces. The proposed approach comprises six phases. The first phase entails data gathering and pre-processing, responsible for collecting and processing data from multiple sources. The second phase involves dataset generation, comprising the creation of a synthetic cce-specific dataset. The third phase focuses on feature enrichment, aiming to augment the avws access log extracted from vm activity and resource logs to enhance the detection of http-get ddos attacks. The fourth phase entails dataset validation, aimed at validating the dataset to ensure its validity and readiness, and confirming that it meets the requirements of a benchmark dataset. The fifth phase involves ensemble feature selection, aimed at selecting the most crucial and minimal feature set that contributes to detecting http-get ddos attacks. The sixth phase aims to develop a deep learning detection model based on long short-term memory (lstm) to detect http-get ddos attacks on cce accurately. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/24488 | |
| dc.language.iso | en | |
| dc.subject | Collaborative-Based Approach Utilizing Ensemble Feature Selection | |
| dc.title | Collaborative-Based Approach Utilizing Ensemble Feature Selection For Detecting Http-Get Ddos Attacks In Cloud Computing Environments | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |