Ashhab, Ziyad Reefat Hamzeh Al (2025) Collaborative-Based Approach Utilizing Ensemble Feature Selection For Detecting Http-Get Ddos Attacks In Cloud Computing Environments. PhD thesis, Perpustakaan Hamzah Sendut.
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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.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis |
| Depositing User: | Mr Hasmizar Mansor |
| Date Deposited: | 25 Jun 2026 06:44 |
| Last Modified: | 25 Jun 2026 06:45 |
| URI: | http://eprints.usm.my/id/eprint/64467 |
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