Two-steps feature selection for detection variant distributed denial of services attack in cloud environment
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Two-steps feature selection for detection variant distributed denial of services attack in cloud environment |
| 2. | Creator | Author's name, affiliation, country | Kurniabudi Kurniabudi; Universitas Dinamika Bangsa; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Eko Arip Winanto; Universitas Dinamika Bangsa; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Sharipuddin Sharipuddin; Universitas Dinamika Bangsa; Indonesia |
| 3. | Subject | Discipline(s) | Information Security; Machine Learning |
| 3. | Subject | Keyword(s) | Attack detection; Classification; DDoS; Feature selection; Machine learning |
| 4. | Description | Abstract | The prevalence of cloud computing among organizations poses a significant problem in ensuring security. Specifically, distributed denial of services (DDoS) attacks targeting cloud computing networks can lead to financial losses for consumers of cloud computing services. This assault has the potential to render cloud services inaccessible. The detection system serves as a remedy to prevent more substantial losses. This research aims to enhance the efficacy of the system detection model by integrating feature selection with three machine learning algorithms: decision tree (DT), random forest (RF), and naïve Bayes (NB). Therefore, our study suggests combining two phases of feature selection into the DDoS attack detection procedure. The first phase uses the information gain (IG) feature selection technique approach, and the second phase uses the principal component analysis (PCA) feature extraction approach. The technique is referred to as two-step feature selection. The test findings indicate that the implementation of two-step feature selection can enhance the performance of the DT and RF detection models by around 9%. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2025-10-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijai.iaescore.com/index.php/IJAI/article/view/26749 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijai.v14.i5.pp3945-3957 |
| 11. | Source | Title; vol., no. (year) | IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 5: October 2025 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2025 Kurniabudi, Eko Arip Winanto, Sharipuddin This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |