Teaching Johnny Not to Fall for Phish

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Teaching Johnny Not to Fall for Phish
Jason Hong, PhDCarnegie Mellon
UniversityWombat Security Technologies
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Everyday Privacy and Security Problem
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This entire process known as phishing
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How Bad Is Phishing?Consumer Perspective

• Estimated 0.5 of Internet users per year fall
  for phishing attacks
• Conservative 1B direct losses a year to
  consumers
• Bank accounts, credit card fraud
• Doesnt include time wasted on recovery of funds,
  restoring computers, emotional uncertainty
• Growth rate of phishing
• 30k reported unique emails / month
• 45k reported unique sites / month
• Social networking sites now major targets

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How Bad Is Phishing?Perspective of Corporations

• Direct damage
• Loss of sensitive customer data

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How Bad Is Phishing?Perspective of Corporations

• Direct damage
• Loss of sensitive customer data
• Loss of intellectual property

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How Bad Is Phishing?Perspective of Corporations

• Direct damage
• Loss of sensitive customer data
• Loss of intellectual property
• Fraud
• Disruption of network services
• Indirect damage
• Damage to reputation, lost sales, etc
• Response costs (call centers, recovery)
• One bank estimated it cost them 1M per phishing
  attack

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Phishing Increasing in SophisticationTargeting
Your Organization

• Spear-phishing targets specific groups or
  individuals
• Type 1 Uses info about your organization

General Patton is retiring next week, click here
to say whether you can attend his retirement
party
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Phishing Increasing in SophisticationTargeting
Your Organization

• Around 40 of people in our experiments at CMU
  would fall for emails like this (control
  condition)

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Phishing Increasing in SophisticationTargeting
You Specifically

• Type 2 Uses info specifically about you
• Social phishing
• Might use information from social networking
  sites, corporate directories, or publicly
  available data
• Ex. Fake email from friends or co-workers
• Ex. Fake videos of you and your friends

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Phishing Increasing in SophisticationTargeting
You Specifically
Heres a video I took of yourposter presentation.
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Phishing Increasing in SophisticationTargeting
You Specifically

• Type 2 Uses info specifically about you
• Whaling focusing on big targets

Thousands of high-ranking executives across the
country have been receiving e-mail messages this
week that appear to be official subpoenas from
the United States District Court in San Diego.
Each message includes the executives name,
company and phone number, and commands the
recipient to appear before a grand jury in a
civil case. -- New York Times Apr16 2008
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Phishing Increasing in SophisticationCombination
with Malware

• Malware and phishing are becoming combined
• Poisoned attachments (Ex. custom PDF exploits)
• Links to web sites with malware (web browser
  exploits)
• Can install keyloggers or remote access software

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Protecting People from Phishing

• Human side
• Interviews and surveys to understand
  decision-making
• PhishGuru embedded training
• Micro-games for security training
• Understanding effectiveness of browser warnings
• Computer side
• PILFER email anti-phishing filter
• CANTINA web anti-phishing algorithm
• Machine learning of blacklists
• Social web machine learning to combat scams

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Results of Our Research

• Startup
• Customers of micro-games featured include
  governments, financials, universities
• Our filter is labeling several million emails
  per day
• Study on browser warnings -gt MSIE8
• Elements of our work adopted by Anti-Phishing
  Working Group (APWG)
• Popular press article in Scientific American

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Outline of Rest of Talk

• Rest of talk will focus on educating end-users
• PhishGuru embedded training
• Anti-Phishing Phil micro-game
• Anti-Phishing Phyllis micro-game

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User Education is Challenging

• Users are not motivated to learn about security
• Security is a secondary task
• Difficult to teach people to make right online
  trust decision without increasing false positives
• User education is a complete waste of time. It
  is about as much use as nailing jelly to a wall.
  They are not interestedthey just want to do
  their job.
• Martin Overton, IBM security specialist
  http//news.cnet.com/21007350_361252132.html

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But Actually, Users Are Trainable

• Our research demonstrates that users can learn
  techniques to protect themselves from phishing
  if you can get them to pay attention to training
• P. Kumaraguru, S. Sheng, A. Acquisti, L. Cranor,
  and J. Hong. Teaching Johnny Not to Fall for
  Phish. CyLab Technical Report CMU CyLab07003,
  2007.

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How Do We Get People Trained?

• Solution
• Find teachable moments PhishGuru
• Make training fun Anti-Phishing Phil,
  Anti-Phishing Phyllis
• Use learning science principles

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PhishGuru Embedded Training

• Send emails that look like a phishing attack
• If recipient falls for it, show intervention that
  teaches what cues to look for in succinct and
  engaging format
• Multiple user studies have demonstrated that
  PhishGuru is effective
• Delivering same training via direct email is not
  effective!

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Subject Revision to Your Amazon.com Information
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Subject Revision to Your Amazon.com Information
Please login and enter your information
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Evaluation of PhishGuru

• Is embedded training effective?
• Study 1 Lab study, 30 participants
• Study 2 Lab study, 42 participants
• Study 3 Field trial at company, 300
  participants
• Study 4 Field trial at CMU, 500 participants
• Studies showed significant decrease in falling
  for phish and ability to retain what they learned
• P. Kumaraguru et al. Protecting People from
  Phishing The Design and Evaluation of an
  Embedded Training Email System. CHI 2007.
• P. Kumaraguru et al. Getting Users to Pay
  Attention to Anti-Phishing Education Evaluation
  of Retention and Transfer. eCrime 2007.

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Study 4 at CMU

• Investigate effectiveness and retention of
  training after 1 week, 2 weeks, and 4 weeks
• Compare effectiveness of 2 training messages vs
  1 training message
• Examine demographics and phishing
• P. Kumaraguru, J. Cranshaw, A. Acquisti, L.
  Cranor, J. Hong, M. A. Blair, and T. Pham.
  School of Phish A Real-World Evaluation of
  Anti-Phishing Training. 2009. SOUPS 2009.

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Study design

• Sent email to all CMU students, faculty and
  staff to recruit participants (opt-in)
• 515 participants in three conditions
• Control / One training message / Two messages
• Emails sent over 28 day period
• 7 simulated spear-phishing messages
• 3 legitimate (cyber security scavenger hunt)
• Campus help desks and IT departments notified
  before messages sent

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Effect of PhishGuru Training
Condition N who clicked on Day 0 who clicked on Day 28
Control 172 52.3 44.2
Trained 343 48.4 24.5
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Discussion of PhishGuru

• PhishGuru can teach people to identify phish
  better
• People retain the knowledge
• People trained on first day less likely to be
  phished
• Two training messages work better
• People werent less likely to click on legitimate
  emails
• People arent resentful, many happy to have
  learned
• 68 out of 85 surveyed said they recommend CMU
  continue doing this sort of training in future
• I really liked the idea of sending CMU students
  fake phishing emails and then saying to them,
  essentially, HEY! You could've just gotten
  scammed! You should be more careful -- here's
  how....
• Contrast to US DOJ and Guam

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APWG Landing Page

• CMU and Wombat helped Anti-Phishing Working Group
  develop landing page for taken down sites
• Already in use by several takedown companies
• Seen by 200,000 people in past 20 months

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Anti-Phishing Phil

• A micro-game to teach people not to fall for
  phish
• PhishGuru about email, this game about web
  browser
• Also based on learning science principles
• Goals
• How to parse URLs
• Where to look for URLs
• Use search engines for help
• Try the game!
• Search for phishing game
• S. Sheng et al. Anti-Phishing Phil The Design
  and Evaluation of a Game That Teaches People Not
  to Fall for Phish. In SOUPS 2007, Pittsburgh, PA,
  2007.

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Anti-Phishing Phil
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Evaluation of Anti-Phishing Phil

• Is Phil effective? Yes!
• Study 1 56 people in lab study
• Study 2 4517 people in field trial
• Brief results of Study 1
• Phil about as effective in helping people detect
  phishing web sites as paying people to read
  training material
• But Phil has significantly fewer false positives
  overall
• Suggests that existing training material making
  people paranoid about phish rather than
  differentiating

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Evaluation of Anti-Phishing Phil

• Study 2 4517 participants in field trial
• Randomly selected from 80000 people
• Conditions
• Control Label 12 sites then play game
• Game Label 6 sites, play game, then label 6
  more, then after 7 days, label 6 more (18 total)
• Participants
• 2021 people in game condition, 674 did retention
  portion

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Anti-Phishing Phil Study 2

• Novices showed most improvement in false
  negatives (calling phish legitimate)

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Anti-Phishing Phil Study 2

• Improvement all around for false positives

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Anti-Phishing Phyllis

• New micro-game just released by Wombat Security
• Focuses on teaching people about what cues to
  look for in emails
• Some emails are legitimate, some fake
• Have to identify cues as dangerous or harmless

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Summary

• Phishing is already a plague on the Internet
• Seriously affects consumers, businesses,
  governments
• Criminals getting more sophisticated
• End-users can be trained, but only if done right
• PhishGuru embedded training uses simulated
  phishing
• Anti-Phishing Phil and Anti-Phishing Phyllis
  micro-games
• Can try PhishGuru, Phil, and Phyllis
  at www.wombatsecurity.com
• Will show free demo of Phil and Phyllis to anyone
  who can explain to me whats going on in Lost

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Acknowledgments

• Ponnurangam Kumaraguru
• Steve Sheng
• Lorrie Cranor
• Norman Sadeh

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Screenshots
Internet Explorer Passive Warning
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Screenshots
Internet Explorer Active Block
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Screenshots
Mozilla FireFox Active Block
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How Effective are these Warnings?

• Tested four conditions
• FireFox Active Block
• IE Active Block
• IE Passive Warning
• Control (no warnings or blocks)
• Shopping Study
• Setup some fake phishing pages and added to
  blacklists
• We phished users after purchases (2 phish/user)
• Real email accounts and personal information
• S. Egelman, L. Cranor, and J. Hong. You've Been
  Warned An Empirical Study of the Effectiveness
  of Web Browser Phishing Warnings. CHI 2008.

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How Effective are these Warnings?
Almost everyone clicked, even those with
technical backgrounds
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How Effective are these Warnings?
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Discussion of Phish Warnings

• Nearly everyone will fall for highly contextual
  phish
• Passive IE warning failed for many reasons
• Didnt interrupt the main task
• Slow to appear (up to 5 seconds)
• Not clear what the right action was
• Looked too much like other ignorable warnings
  (habituation)
• Bug in implementation, any keystroke dismisses

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Screenshots
Internet Explorer Passive Warning
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Discussion of Phish Warnings

• Active IE warnings
• Most saw but did not believe it
• Since it gave me the option of still proceeding
  to the website, I figured it couldnt be that
  bad
• Some element of habituation (looks like other
  warnings)
• Saw two pathological cases

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Screenshots
Internet Explorer Active Block
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Internet Explorer 8 Re-design
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A Science of Warnings

• See the warning?
• Understand?
• Believe it?
• Motivated?
• Can and will act?
• Refining this model for computer warnings

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Outline

• Human side
• Interviews and surveys to understand
  decision-making
• PhishGuru embedded training
• Anti-Phishing Phil game
• Understanding effectiveness of browser warnings
• Computer side
• PILFER email anti-phishing filter
• CANTINA web anti-phishing algorithm
• Machine learning of blacklists

Can we improve phish detection of web sites?
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Detecting Phishing Web Sites

• Industry uses blacklists to label phishing sites
• But blacklists slow to new attacks
• Idea Use search engines
• Scammers often directly copy web pages
• But fake pages should have low PageRank on search
  engines
• Generate text-based fingerprint of web page
  keywords and send to a search engine
• Y. Zhang, S. Egelman, L. Cranor, and J. Hong
  Phinding Phish Evaluating Anti-Phishing Tools.
  In NDSS 2007.
• Y. Zhang, J. Hong, and L. Cranor. CANTINA A
  content-based approach to detecting phishing web
  sites. In WWW 2007.
• G. Xiang and J. Hong. A Hybrid Phish Detection
  Approach by Identity Discovery and Keywords
  Retrieval. In WWW 2009.

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Robust Hyperlinks

• Developed by Phelps and Wilensky to solve 404
  not found problem
• Key idea was to add a lexical signature to URLs
  that could be fed to a search engine if URL
  failed
• Ex. http//abc.com/page.html?sigword1word2...
  word5
• How to generate signature?
• Found that TF-IDF was fairly effective
• Informal evaluation found five words was
  sufficient for most web pages

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Fake
eBay, user, sign, help, forgot
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Real
eBay, user, sign, help, forgot
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Evaluating CANTINA
PhishTank
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Machine Learning of Blacklists

• Human-verified blacklists maintained by
  Microsoft, Google, PhishTank
• Pros Reliable, extremely low false positives
• Cons Slow to respond, can be flooded with URLs
  (fast flux)
• Observation 1 many phishing sites similar
• Constructed through toolkits
• Observation 2 many phishing sites similar
• Fast flux (URL actually points to same site)
• Idea Rather than just examining URL, compare
  content of a site to known phishing sites

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Machine Learning of Blacklists

• Approach 1 Use hashcodes of web page
• Simple, good against fast flux
• Easy to defeat (though can allow some
  flexibility)
• Approach 2 Use shingling
• Shingling is an approach used by search engines
  to find duplicate pages
• connect with the eBay community -gt connect
  with the, with the eBay, the eBay community
• Count the number of common shingles out of total
  shingles, set threshold

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Machine Learning of Blacklists

• Use Shingling
• Protect against false positives
• Phishing sites look a lot like real sites
• Have a small whitelist (ebay, paypal, etc)
• Use CANTINA too

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Tells people why they are seeing this message,
uses engaging character
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Tells a story about what happened and what the
risks are
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Gives concrete examples of how to protect oneself
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Explains how criminals conduct phishing attacks
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