원문정보
초록
영어
The phishing is a kind of e-commerce lure which is intended to steal the confidential information of the internet user by making identical website of legitimate one in which the contents and images most likely remains similar to the legitimate website. The other way of phishing website is to do minor changes in the URL or in the domain of the website. In this paper, an anti-phishing system is proposed which is based on the development of the Add-on tool for the web browser. The performance of the proposed system is studied with four different data mining classification algorithms which are Class Imbalance Problem (CIP), Rule based Classifier (Sequential Covering Algorithm (SCA)), Nearest Neighbour Classification (NNC), Bayesian Classifier (BC). To evaluate the performance of the proposed anti-phishing system for the detection of phishing websites, we have collected 7690 legitimate websites and 2280 phishing websites from the authorised sources like APWG database and PhishTank. After the analysis of the anti-phishing system, more than 90 percentage successful results achieved at different time periods.
목차
1. Introduction
2. Methods of Phishing Attack
3. Overview of Previous Study on Phishing
4. Anti-Phishing Toolbars
5. Disadvantages of the Existing Systems
6. System design and Development Model
7. Working Procedure of Novel Anti-Phishing System
8. Data Mining Analysis
8.1. Study of the Anti-Phishing Tool with Class Imbalance Problem
8.2. Study of the Anti-Phishing tool with Rule based Classifier Method
8.3. Study of the Anti-Phishing tool with Nearest Neighbour Classification
8.4. Study of the Anti-Phishing tool with Bayesian Classifier
9. Result and Conclusion
Acknowledgments
References