TOWARDS DETECTING PHISHING

WEB-PAGES

Presented by,

Md. Merazul Islam (0507036)

&

Shuvradeb Barman Srijon (0507044)

Supervised by,

Mr. Muhammad Sheikh Sadi

Assistant Professor

Department of Computer Science and Engineering

Khulna University of Engineering and Technology

Khulna 9203, Bangladesh.

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INTRODUCTION Cyber Crime- the major concern. Internet frauds affect the rapidly growing

online services. E-commerce is the main target. Social communication sites and mail services

are also victim of them. Phishing is an alarming threat. Technical steps needed to defend them.

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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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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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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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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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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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METHODOLOGY

Step 1: Checking with database

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METHODOLOGY

Step 2: Checking abnormal conditions

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METHODOLOGY

Step 2: Search for new Phishing

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RESULTS

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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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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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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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THANK YOU

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