E3S Web Conf.
Volume 399, 2023International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|Number of page(s)||12|
|Published online||12 July 2023|
An Efficient Approach to Detect Fraudulent Service Enrollment Websites with Novel Random Forest and Compare the Accuracy with XGBoost Machine Algorithm
1 Research Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Pincode: 602105
2 Project Guide, Corresponding Author, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Pincode: 602105
* Corresponding author: Megnasaveetha2022@gmail.com
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<0.05) is statistically significant for detecting fraud websites. The Novel Random Forest algorithm demonstrates higher accuracy in recognizing objects and enhancing the evaluated data, with an accuracy rate of 92.634%, compared to the XGBoost classifier which achieves an accuracy of 75.545%. Conclusion: The accuracy of Novel Random Forest is better when compared to accuracy of XGBoost classifier.
Key words: Novel Random Forest / XGBoost Classifier / Machine Learning Algorithms / Fraud Detection / Website Detection System / Neural Network / Social Protection
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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