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.
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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.
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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.
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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.
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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)
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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.
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8. METHODOLOGY
Step 1: Checking with database
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9. METHODOLOGY
Step 2: Checking abnormal conditions
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10. METHODOLOGY
Step 2: Search for new Phishing
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11. RESULTS
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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
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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.
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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.
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15. THANK YOU
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