Towards detecting phishing web pages
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This document discusses the problem of phishing attacks and proposes an automated method for detecting phishing sites. It begins by outlining the threats from cybercrime, internet fraud, and phishing. It then analyzes the problem of phishing attacks succeeding when users fail to detect phishing sites. The document reviews previous anti-phishing approaches and their limitations. It proposes developing an automated phishing detection method and outlines the methodology, which involves checking URLs against databases and for abnormal conditions. Experimental results show the approach has high accuracy rates. The discussion considers performance improvements and limitations regarding flash content.
Towards detecting phishing web pages
- 2. Cyber Crime- the major concern. Internet frauds affect the rapidly growing online services. E-commerce is the main target. Social communication sites and mail service are also victim of them. Phishing is an alarming threat. Technical steps needed to defend them. 2
- 3. PROBLEM STATEMENT Phishing attacks succeed if users fail to detect phishing sites. Previous anti-phishing falls into four categories: Study on phishing Training people User interface Detection tools Previous works deals with limited service. Our approach- Development of an automated phishing detection method. 3
- 4. PHISHING? A criminal trick of stealing sensitive personal information. Fooled user and push them to fall in the trick. Use social engineering and technical strategy. Mainly, duplicate original web-pages. First describe in 1987. 4
- 5. ATTRIBUTES OF PHISHING Similar appearance of web-page. IP based URL & Non Matching URL. URL contain abnormal characters. Misspelled URL. Using script or add-in to web browser to cover the address bar. 5
- 6. PHISHING STATS According to APWG According to PhishTank Phishes Verified as Valid Suspected Phishes Submitted Total 531086 Total 928206 Online 2770 Online 3021 Offline 528316 Offline 925174 Total phishing attack. (Up to 6th April 2010) 6
- 7. ANTI-PHISHING Social response Educating people. Changing habit. Technical support Identify phishing site. Implementation of secure model. Browser alert. Eliminating phishing mails. Monitoring and Takedown. 7
- 8. METHODOLOGY Step 1: Checking with database 8 ? ?
- 9. METHODOLOGY Step 2: Checking abnormal conditions 9 ? ? ?
- 10. METHODOLOGY Step 2: Search for new Phishing 10 ? ? ?? ?
- 11. RESULTS 11
- 12. EXPERIMENTAL ANALYSIS Approach Accuracy Time (second) IP based URL 100% 17 Exists in phishing database 97% 59 Matching source content 81% 134 Abnormal condition 79% 51 12
- 13. DISCUSSION Our approach reduces the ability of attackers to automate their attacks, cutting into their profitability. By using the minimal knowledge base provided by the user-selected web-page, our system is able to compare potential phishing sites with real sites. Performance and accuracy can be improved by using an image segmentation algorithm. Flash contents can’t be validated whether phishing threat or not in our system. 13
- 14. REFERENCES Anti-Phishing Working Group (APWG). http://www.antiphishing.org/ . April 7 2010. PhishTank. http://www.phishtank.com/. April 6 2010. Y. Zhang, J. Hong, and L. Cranor. Cantina: A content-based approach to detecting phishing web sites. 16th international conference on World Wide Web in 2007. Felix, Jerry and Hauck, Chris (September 1987). "System Security: A Hacker's Perspective". 1987 Interex Proceedings 1: 6. 14
- 15. THANK YOU 15