Fake Website Detection Using Machine Learning Algorithms
Md Sajadul Islam, Mst. Nusrat Jahan Jyoti, Md. Solaiman Mia, Md Gulzar Hussain · 2023
Fake websites have become a growing concern in today’s digital age, as they are designed to deceive users into sharing personal and financial information. This research investigates the performance of Machine Learning algorithms, including Random Forest, LightGBM, and XGBoost, for detecting fake websites. We have utilized a categorical dataset with four types of websites: benign, defacement, phishing, and malware, and extracted several features from website content and metadata to train and test the algorithms. The results show that Random Forest achieved the highest accuracy (97%), outperforming both LightGBM (96%) and XGBoost (96.2%). This study highlights the effectiveness of using ensemble learning algorithms for detecting fake websites and continued research in this area can improve their performance and help safeguard against digital threats.