Phi.sh/$oCiaL: The Phishing Landscape

through Short URLsSidharth Chhabra*, Anupama Aggarwal†, Fabricio Benevenuto‡, Ponnurangam Kumaraguru†

*Delhi College of Engineering, †IIIT-Delhi, †Federal University of Ouro Preto

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Motivation

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4

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Phishing via Short URLs

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•Most popular - June 2010 - January 2011 *

•Most abused URL shortener

•23.48% of short URL services

http://techblog.avira.com/en/

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Research Aim

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•Analysis of Phishing Tweets containing Bitly

• How is Bitly used by Phishers?

• Who is Targeted ?

• Which Locations are Affected ?

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System Architecture

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Referral Analysis

URL

Time

Is a Phish

Is Up

Phishing

URLs

Short

URLsLong URL

Short URL

Created by

Lookup API

Brand Analysis

Temporal Analysis

Geographical Analysis

Behavioral

Analysis

Text Analysis

Network Analysis

Data Collection Filtering

Analysis

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Vote if Phishing

Yes No Unknown

Online

Yes 11,081 392 1,234

No1,02,17

55,991 68,731

Unknown 4,863 523 795

1 January - 31 December, 2010

Dataset

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Dataset

• 990 public Twitter users who posted phish tweets

• 864 user accounts present at the time of analysis

• 2000 past tweets for each of 516 users

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Results

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Space gain is fraction of space saved by using bit.lyFor 50% URLs, Space Gain < 37%

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Social Network Websites targeted

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516Twitterusers

213 inorganic

303 organic

153 compromised

150 legitimate

Phish activity is majorly automated

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Sparse Network, High Reciprocity

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Country was determined by using the Bit.ly statistics

Brazil is most targeted followed by US and Canada

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Limitations

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•Reliance on PhishTank

•90% URLs offline when voted

•Small number of active voters

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Conclusion

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•URLs shorteners used to hide identity

•Change in landscape of phishing - OSNs target

•Phishing activity is automated

•Lack of phishing communities

•Brazil had highest phish URL clickthrough

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Future Work

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•Analyze the use of URL shorteners like goo.gl, tinyurl etc.

•Develop an algorithm to detect phishing on Twitter

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Thank You !http://precog.iiitd.edu.in