Phishing Website Detection System

Phishing Website Detection System

Tanmay Patil, Yash Mandlik. Rushikesh Sisode, Prof. P S Sawale

Department of Computer Engineering, Smt Kashibai Navale College of Engineering, Pune

Abstract

Phishing attacks have become increasingly sophisticated, posing a persistent threat to cybersecurity by deceiving users into revealing sensitive information like login credentials and financial data. This paper introduces an advanced phishing detection system utilizing the Random Forest algorithm for high-accuracy classification and real-time detection of phishing websites. By leveraging machine learning techniques and URL feature extraction, the system is capable of analyzing critical attributes such as URL structure, special characters, and domain data to identify fraudulent sites. The MERN stack is employed to create a seamless and user-friendly frontend, allowing users to input URLs and receive immediate feedback on legitimacy. This integration ensures superior performance over traditional models like SVM and Naïve Bayes, addressing key shortcomings such as false-positive rates and poor scalability. The proposed system offers a robust solution to phishing threats, significantly improving user safety and trust in online interactions.

Keywords: Phishing Detection, Random Forest, MERN Stack, Machine Learning, URL Analysis, Cybersecurity. Keywords: Phishing Detection, Random Forest, MERN Stack, URL Feature Extraction, Machine Learning, Cybersecurity.