Detecting E Banking Phishing Websites Using Associative Classification

Detecting E Banking Phishing Websites Using Associative Classification

Abstract:

An sophisticated and flexible system based on classification data mining algorithms detects and predicts e-banking phishing websites. Classification methods extract phishing data sets and classify e-banking websites. URL, domain, security, and encryption parameters determine the ultimate phishing detection rate. The system uses data mining algorithms to improve classification performance and protect user transactions. This article examines system pros and cons.

Introduction:

Online transactions and e-banking are becoming more prevalent, raising concerns about data security. Phishing websites pose as e-banking platforms to steal usernames, passwords, and credit card information. Phishing websites must be detected and prevented to protect user data and internet security. Classification data mining techniques detect and predict e-banking phishing websites in this study.

Objective:

This project aims to create a system that can recognize e-banking phishing websites and safeguard online transactions. The system classifies e-banking websites based on URL and domain identity, security, and encryption using classification and data mining methods. The research compares the suggested data mining method to traditional classification algorithms to prove its efficacy.

Project Details:

Classification data mining techniques extract and evaluate phishing data in the suggested system. By training the system with known phishing websites and real e-banking platforms, it learns to spot crucial differences. The technology checks URL and domain identification for phishing trends.

The technology uses a collection of phishing websites and authentic e-banking systems. Websites are analyzed for URL structure, domain age, SSL certificate validity, and encryption techniques. The classification system uses these features to learn phishing website patterns. After training, the system can verify new e-banking websites.

The trained classification algorithm evaluates the e-banking website in real time throughout a transaction. It checks the website’s URL, domain identity, and security measures for phishing. The technology alerts users to avoid inputting sensitive information if phishing is likely.

Conclusion:

Classification data mining algorithms enable the suggested intelligent system to recognize and anticipate e-banking phishing websites. The technology uses algorithms to precisely classify e-banking platforms, allowing customers to safely perform online transactions. This system improves e-commerce customer relationship management, secure online payments, and classification algorithm performance.