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Towards detecting phishing web pages

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Towards detecting phishing web pages

  1. 1.  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
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. METHODOLOGY Step 1: Checking with database 8 ? ?
  8. 8. METHODOLOGY Step 2: Checking abnormal conditions 9 ? ? ?
  9. 9. METHODOLOGY Step 2: Search for new Phishing 10 ? ? ?? ?
  10. 10. RESULTS 11
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. THANK YOU 15

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