Phishing Website Detection Using Machine Learning Methods
Sudhir S. Anakal, Kiran Maka, Arun Tadkal, Sunil Humanabad, Sridhar Anakal, E Laxmikant · 2023
As the internet promotes the growth of ecommerce businesses, the security of financial and personal data becomes a concern. Phishing attacks are becoming more complex and difficult to detect. Several Machine Learning (ML) algorithms that gather data from multiple sources, such as website addresses, search engines, and other internet resources, might be useful in distinguishing a legitimate website from a phishing website. This paper investigates supervised ML techniques such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gradient Boosting (GB), and AdaBoost that are used to detect phishing websites. After training the ML models, the top-performing ML model is deployed using Streamlit so that it can be accessed to check the legitimacy of the website before it is accessed.