Phishing Website URL’s Detection Using NLP and Machine Learning Techniques
Dinesh Kalla, Sivaraju Kuraku
Abstract
Phishing websites present a severe cybersecurity risk since they can lead to financial losses, data breaches, and user privacy violations. This study uses machine learning approaches to solve the problem of phishing website detection. Using artificial intelligence, the project aims to provide efficient techniques for locating and thwarting these dangerous websites. The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification. Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Classifiers, Linear Support Vector Classifiers, and Naive Bayes were all used in the inquiry. This research covers the benefits and drawbacks of several Machine Learning approaches, illuminating how well-suited each is to overcome the difficulties in locating and countering phishing website predictions. The insights gained from this literature review guide the selection and implementation of appropriate models and methods in future research and real-world applications related to phishing detections. The study evaluates and compares accuracy, precision and recalls of several machine learning models in detecting phishing website URL’s detection.