| Autor: |
S Meghana, R Senthil Kumar |
| Jazyk: |
English<br />French |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
E3S Web of Conferences, Vol 399, p 04022 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2267-1242 |
| DOI: |
10.1051/e3sconf/202339904022 |
| Popis: |
Aim: The main aim of this research study is to detect fraudulent service enrollment websites using the Novel Random Forest algorithm and compare its accuracy with the XGBoost classifier algorithm. Materials and Methods: This research involved comparing two groups namely Random Forest and XGBoost. In this study, 1784 dataset samples had been utilized for statistical analysis. Dataset splits into training and testing which have 1200 of training and 584 of testing. The Gpower test was utilized with a setting parameter of 85% (α=0.05 and power=0.85) to determine the appropriate sample size. With a sample size of 10 and a confidence interval of 95%, we aimed to predict fraudulent service enrollment websites. Results: The significant value of p=0.000 (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
