Breaking the Kubernetes Kill Chain: Host Path Mount
Towards detecting phishing web pages
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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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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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